-------------------------------------------------------------------------------------
      name:  <unnamed>
       log:  /Users/abhram/Library/CloudStorage/Dropbox/Research/trA/replication/tabl
> e_4.log
  log type:  text
 opened on:  10 May 2023, 17:51:41

.         
. ******************************************************************** 
. ********         TABLE 4:  POOLED GQR                       ********
. ******************************************************************** 
. 
. 
. 
.         use "Duranton_Turner_AER_2010.dta", clear

. 
. 
.         * Selection of observations
.         drop if l_ln_km_IH_83==0
(47 observations deleted)

. 
.         rename sprawl_1992 sprawl_1993

.         rename sprawl_1976 sprawl_1983

.         gen sprawl_2003 = sprawl_1993

. 
.         *rescale so display is better
.         replace elevat_range_msa = elevat_range_msa/1000 
variable elevat_range_msa was int now float
(228 real changes made)

.         replace ruggedness_msa = ruggedness_msa/1000
(228 real changes made)

.         replace heating_dd = heating_dd/100
(228 real changes made)

.         replace cooling_dd = cooling_dd/100
(228 real changes made)

. 
.       rename S_somecollege_80 S_somecollege_1983

.       rename S_somecollege_00 S_somecollege_2003

.       rename S_somecollege_90 S_somecollege_1993

.       rename S_poor_80 S_poor_1983

.       rename S_poor_90 S_poor_1993

.       rename S_poor_00 S_poor_2003

.       rename l_mean_income_80 l_mean_income_1983

.       rename l_mean_income_90 l_mean_income_1993

.       rename l_mean_income_00 l_mean_income_2003

.       rename S_manuf83 S_manuf_1983

.       rename S_manuf93 S_manuf_1993

.       rename S_manuf03 S_manuf_2003

.       rename S_truck83 S_truck_1983

.       rename S_truck93 S_truck_1993

.       rename S_truck03 S_truck_2003

.       gen l_pop_1983 = l_pop80 

.       gen l_pop_1993 = l_pop90 

.       gen l_pop_2003 = l_pop00 

. 
.       rename l_max_84bus l_bus_1983 

.       rename l_max_94bus l_bus_1993 

.       rename l_max_04bus l_bus_2003 

.       rename l_transit84 l_transit_1983

.       rename l_transit94 l_transit_1993 

.       rename l_transit04 l_transit_2003

. 
.       rename l_ln_km_IHU_83 l_ln_km_IHU_1983

.       rename l_ln_km_IHU_93 l_ln_km_IHU_1993

.       rename l_ln_km_IHU_03 l_ln_km_IHU_2003

.       rename l_ln_km_IH_83 l_ln_km_IH_1983

.       rename l_ln_km_IH_93 l_ln_km_IH_1993

.       rename l_ln_km_IH_03 l_ln_km_IH_2003

.       rename l_ln_km_MRU_83 l_ln_km_MRU_1983

.       rename l_ln_km_MRU_93 l_ln_km_MRU_1993

.       rename l_ln_km_MRU_03 l_ln_km_MRU_2003

.       
.       rename l_vmt_IHU_83 l_vmt_IHU_1983

.       rename l_vmt_IHU_93 l_vmt_IHU_1993

.       rename l_vmt_IHU_03 l_vmt_IHU_2003

.       rename l_vmt_IH_83 l_vmt_IH_1983

.       rename l_vmt_IH_93 l_vmt_IH_1993

.       rename l_vmt_IH_03 l_vmt_IH_2003

.       rename l_vmt_MRU_83 l_vmt_MRU_1983

.       rename l_vmt_MRU_93 l_vmt_MRU_1993

.       rename l_vmt_MRU_03 l_vmt_MRU_2003

. 
. 
. 
.         reshape long l_ln_km_IH  l_ln_km_IHU  l_ln_km_IHNU  l_ln_km_MRU l_vmt_IH l_
> vmt_IHU  l_vmt_IHNU  l_vmt_MRU l_bus l_transit sprawl S_somecollege l_mean_income S
> _poor S_manuf S_truck l_pop, i(msa ) j(year _1983 _1993 _2003)
(variable l_ln_km_IHNU_1983 not found)
(variable l_vmt_IHNU_1983 not found)
(variable l_ln_km_IHNU_1993 not found)
(variable l_vmt_IHNU_1993 not found)
(variable l_ln_km_IHNU_2003 not found)
(variable l_vmt_IHNU_2003 not found)

Data                               Wide   ->   Long
-----------------------------------------------------------------------------
Number of observations              228   ->   684         
Number of variables                 217   ->   190         
j variable (3 values)                     ->   year
xij variables:
l_ln_km_IH_1983 l_ln_km_IH_1993 l_ln_km_IH_2003->l_ln_km_IH
l_ln_km_IHU_1983 l_ln_km_IHU_1993 l_ln_km_IHU_2003->l_ln_km_IHU
l_ln_km_IHNU_1983 l_ln_km_IHNU_1993 l_ln_km_IHNU_2003->l_ln_km_IHNU
l_ln_km_MRU_1983 l_ln_km_MRU_1993 l_ln_km_MRU_2003->l_ln_km_MRU
l_vmt_IH_1983 l_vmt_IH_1993 l_vmt_IH_2003 ->   l_vmt_IH
l_vmt_IHU_1983 l_vmt_IHU_1993 l_vmt_IHU_2003-> l_vmt_IHU
l_vmt_IHNU_1983 l_vmt_IHNU_1993 l_vmt_IHNU_2003->l_vmt_IHNU
l_vmt_MRU_1983 l_vmt_MRU_1993 l_vmt_MRU_2003-> l_vmt_MRU
       l_bus_1983 l_bus_1993 l_bus_2003   ->   l_bus
l_transit_1983 l_transit_1993 l_transit_2003-> l_transit
    sprawl_1983 sprawl_1993 sprawl_2003   ->   sprawl
S_somecollege_1983 S_somecollege_1993 S_somecollege_2003->S_somecollege
l_mean_income_1983 l_mean_income_1993 l_mean_income_2003->l_mean_income
    S_poor_1983 S_poor_1993 S_poor_2003   ->   S_poor
 S_manuf_1983 S_manuf_1993 S_manuf_2003   ->   S_manuf
 S_truck_1983 S_truck_1993 S_truck_2003   ->   S_truck
       l_pop_1983 l_pop_1993 l_pop_2003   ->   l_pop
-----------------------------------------------------------------------------

. 
.         local geography  "elevat_range_msa ruggedness_msa heating_dd cooling_dd spr
> awl"

.         local demographics "S_somecollege l_mean_income S_poor S_manuf"  

.         local census_div "div1 div2 div3 div4 div5 div6 div7 div8 div9"

.         local population "l_pop80 l_pop70 l_pop60 l_pop50 l_pop40 l_pop30 l_pop20" 
>  

. 
. 
.         
. * First Panel * 
.                 
.                 local Inst "l_rail1898 l_hwy1947 l_pix1835"

.                 capture drop l_vmt l_ln

.                 gen l_vmt = l_vmt_IH  

.                 gen l_ln  = l_ln_km_IH 

. 
. 
. 
. * Model 1 *     
. 
.                 xi: ivregress 2sls l_vmt i.year (l_ln = l_rail1898 l_hwy1947 l_pix1
> 835), robust      
i.year            _Iyear_1-3          (_Iyear_1 for year==_1983 omitted)

Instrumental variables 2SLS regression            Number of obs   =        684
                                                  Wald chi2(3)    =    3101.93
                                                  Prob > chi2     =     0.0000
                                                  R-squared       =     0.8742
                                                  Root MSE        =      .4856

------------------------------------------------------------------------------
             |               Robust
       l_vmt | Coefficient  std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
        l_ln |   1.315844   .0258333    50.94   0.000     1.265212    1.366477
    _Iyear_2 |   .3989198   .0469814     8.49   0.000     .3068379    .4910018
    _Iyear_3 |   .6650321   .0463963    14.33   0.000      .574097    .7559672
       _cons |     6.2805   .1725364    36.40   0.000     5.942335    6.618666
------------------------------------------------------------------------------
Endogenous: l_ln
Exogenous:  _Iyear_2 _Iyear_3 l_rail1898 l_hwy1947 l_pix1835

. 
.                 xi: genqreg l_vmt l_ln,  q(10) proneness(i.year) instruments(l_rail
> 1898 l_hwy1947 l_pix1835) optimize(grid) grid1(0.1(0.01)2)        
i.year            _Iyear_1-3          (_Iyear_1 for year==_1983 omitted)
Grid-search optimization
Number of grid points: 191
 
Generalized Quantile Regression (GQR)
     Observations:                684
 
------------------------------------------------------------------------------
       l_vmt | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
        l_ln |       1.39   .2242528     6.20   0.000     .9504726    1.829527
------------------------------------------------------------------------------
Excluded intstruments: l_rail1898 l_hwy1947 l_pix1835
Proneness variables: _Iyear_2 _Iyear_3

.                 xi: genqreg l_vmt l_ln,  q(25) proneness(i.year) instruments(l_rail
> 1898 l_hwy1947 l_pix1835) optimize(grid) grid1(0.1(0.01)2)       
i.year            _Iyear_1-3          (_Iyear_1 for year==_1983 omitted)
Grid-search optimization
Number of grid points: 191
 
Generalized Quantile Regression (GQR)
     Observations:                684
 
------------------------------------------------------------------------------
       l_vmt | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
        l_ln |      1.395   .0714292    19.53   0.000     1.255001    1.534999
------------------------------------------------------------------------------
Excluded intstruments: l_rail1898 l_hwy1947 l_pix1835
Proneness variables: _Iyear_2 _Iyear_3
    Note: Alternative solutions exist. See e(solutions).

.                 xi: genqreg l_vmt l_ln,  q(50) proneness(i.year) instruments(l_rail
> 1898 l_hwy1947 l_pix1835) optimize(grid) grid1(0.1(0.01)2)       
i.year            _Iyear_1-3          (_Iyear_1 for year==_1983 omitted)
Grid-search optimization
Number of grid points: 191
 
Generalized Quantile Regression (GQR)
     Observations:                684
 
------------------------------------------------------------------------------
       l_vmt | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
        l_ln |       1.22   .1936597     6.30   0.000      .840434    1.599566
------------------------------------------------------------------------------
Excluded intstruments: l_rail1898 l_hwy1947 l_pix1835
Proneness variables: _Iyear_2 _Iyear_3

.                 xi: genqreg l_vmt l_ln,  q(75) proneness(i.year) instruments(l_rail
> 1898 l_hwy1947 l_pix1835)   optimize(grid) grid1(0.1(0.01)2)  
i.year            _Iyear_1-3          (_Iyear_1 for year==_1983 omitted)
Grid-search optimization
Number of grid points: 191
 
Generalized Quantile Regression (GQR)
     Observations:                684
 
------------------------------------------------------------------------------
       l_vmt | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
        l_ln |       1.26   .1246085    10.11   0.000     1.015772    1.504228
------------------------------------------------------------------------------
Excluded intstruments: l_rail1898 l_hwy1947 l_pix1835
Proneness variables: _Iyear_2 _Iyear_3

.                 xi: genqreg l_vmt l_ln,  q(90) proneness(i.year) instruments(l_rail
> 1898 l_hwy1947 l_pix1835)  optimize(grid) grid1(0.1(0.01)2)      
i.year            _Iyear_1-3          (_Iyear_1 for year==_1983 omitted)
Grid-search optimization
Number of grid points: 191
 
Generalized Quantile Regression (GQR)
     Observations:                684
 
------------------------------------------------------------------------------
       l_vmt | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
        l_ln |        1.3   .0533807    24.35   0.000     1.195376    1.404624
------------------------------------------------------------------------------
Excluded intstruments: l_rail1898 l_hwy1947 l_pix1835
Proneness variables: _Iyear_2 _Iyear_3

. 
. 
. 
. * Model 2 *     
. 
.                 xi: ivregress 2sls l_vmt l_pop i.year  (l_ln = l_rail1898 l_hwy1947
>  l_pix1835), robust                  
i.year            _Iyear_1-3          (_Iyear_1 for year==_1983 omitted)

Instrumental variables 2SLS regression            Number of obs   =        684
                                                  Wald chi2(4)    =    7467.71
                                                  Prob > chi2     =     0.0000
                                                  R-squared       =     0.9339
                                                  Root MSE        =     .35203

------------------------------------------------------------------------------
             |               Robust
       l_vmt | Coefficient  std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
        l_ln |   .9235153   .0628895    14.68   0.000     .8002542    1.046776
       l_pop |   .4049238   .0462049     8.76   0.000     .3143639    .4954836
    _Iyear_2 |    .394456   .0338432    11.66   0.000     .3281246    .4607874
    _Iyear_3 |   .6290067   .0337278    18.65   0.000     .5629015    .6951119
       _cons |   3.676959   .2288344    16.07   0.000     3.228452    4.125467
------------------------------------------------------------------------------
Endogenous: l_ln
Exogenous:  l_pop _Iyear_2 _Iyear_3 l_rail1898 l_hwy1947 l_pix1835

. 
.                 xi: genqreg l_vmt l_ln,  q(10) proneness(l_pop i.year) instruments(
> l_rail1898 l_hwy1947 l_pix1835)  optimize(grid) grid1(0.1(0.01)2)
i.year            _Iyear_1-3          (_Iyear_1 for year==_1983 omitted)
Grid-search optimization
Number of grid points: 191
 
Generalized Quantile Regression (GQR)
     Observations:                684
 
------------------------------------------------------------------------------
       l_vmt | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
        l_ln |        .77   .2324629     3.31   0.001     .3143811    1.225619
------------------------------------------------------------------------------
Excluded intstruments: l_rail1898 l_hwy1947 l_pix1835
Proneness variables: l_pop _Iyear_2 _Iyear_3
    Note: Alternative solutions exist. See e(solutions).

.                 xi: genqreg l_vmt l_ln,  q(25) proneness(l_pop i.year) instruments(
> l_rail1898 l_hwy1947 l_pix1835) optimize(grid) grid1(0.1(0.01)2)   
i.year            _Iyear_1-3          (_Iyear_1 for year==_1983 omitted)
Grid-search optimization
Number of grid points: 191
 
Generalized Quantile Regression (GQR)
     Observations:                684
 
------------------------------------------------------------------------------
       l_vmt | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
        l_ln |      1.205   .1510637     7.98   0.000     .9089206    1.501079
------------------------------------------------------------------------------
Excluded intstruments: l_rail1898 l_hwy1947 l_pix1835
Proneness variables: l_pop _Iyear_2 _Iyear_3
    Note: Alternative solutions exist. See e(solutions).

.                 xi: genqreg l_vmt l_ln,  q(50) proneness(l_pop i.year) instruments(
> l_rail1898 l_hwy1947 l_pix1835)  optimize(grid) grid1(0.1(0.01)2)      
i.year            _Iyear_1-3          (_Iyear_1 for year==_1983 omitted)
Grid-search optimization
Number of grid points: 191
 
Generalized Quantile Regression (GQR)
     Observations:                684
 
------------------------------------------------------------------------------
       l_vmt | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
        l_ln |       1.04   .2118811     4.91   0.000     .6247207    1.455279
------------------------------------------------------------------------------
Excluded intstruments: l_rail1898 l_hwy1947 l_pix1835
Proneness variables: l_pop _Iyear_2 _Iyear_3

.                 xi: genqreg l_vmt l_ln,  q(75) proneness(l_pop i.year) instruments(
> l_rail1898 l_hwy1947 l_pix1835)   optimize(grid) grid1(0.1(0.01)2)    
i.year            _Iyear_1-3          (_Iyear_1 for year==_1983 omitted)
Grid-search optimization
Number of grid points: 191
 
Generalized Quantile Regression (GQR)
     Observations:                684
 
------------------------------------------------------------------------------
       l_vmt | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
        l_ln |         .5   .0777981     6.43   0.000     .3475185    .6524815
------------------------------------------------------------------------------
Excluded intstruments: l_rail1898 l_hwy1947 l_pix1835
Proneness variables: l_pop _Iyear_2 _Iyear_3
    Note: Alternative solutions exist. See e(solutions).

.                 xi: genqreg l_vmt l_ln,  q(90) proneness(l_pop i.year) instruments(
> l_rail1898 l_hwy1947 l_pix1835)   optimize(grid) grid1(0.01(0.02)2)                
>                   
i.year            _Iyear_1-3          (_Iyear_1 for year==_1983 omitted)
Grid-search optimization
Number of grid points: 100
 
Generalized Quantile Regression (GQR)
     Observations:                684
 
------------------------------------------------------------------------------
       l_vmt | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
        l_ln |        .53   .1584645     3.34   0.001     .2194154    .8405846
------------------------------------------------------------------------------
Excluded intstruments: l_rail1898 l_hwy1947 l_pix1835
Proneness variables: l_pop _Iyear_2 _Iyear_3

. 
. 
. 
. * Model 3 *     
. 
.                 xi: ivregress 2sls l_vmt l_pop div2 div3 div4 div5 div6 div7 div8 d
> iv9 elevat_range_msa ruggedness_msa heating_dd cooling_dd sprawl i.year  (l_ln = l_
> rail1898 l_hwy1947 l_pix1835), robust                                     
i.year            _Iyear_1-3          (_Iyear_1 for year==_1983 omitted)

Instrumental variables 2SLS regression            Number of obs   =        684
                                                  Wald chi2(17)   =   12453.28
                                                  Prob > chi2     =     0.0000
                                                  R-squared       =     0.9450
                                                  Root MSE        =     .32124

----------------------------------------------------------------------------------
                 |               Robust
           l_vmt | Coefficient  std. err.      z    P>|z|     [95% conf. interval]
-----------------+----------------------------------------------------------------
            l_ln |   1.031852   .0719744    14.34   0.000     .8907843    1.172919
           l_pop |   .2998401   .0573088     5.23   0.000     .1875169    .4121633
            div2 |  -.1751959   .0616247    -2.84   0.004     -.295978   -.0544137
            div3 |   .0426976   .0733614     0.58   0.561     -.101088    .1864833
            div4 |  -.0481125   .0884504    -0.54   0.586    -.2214722    .1252471
            div5 |  -.0713817   .0877872    -0.81   0.416    -.2434414    .1006779
            div6 |  -.1763737    .092465    -1.91   0.056    -.3576018    .0048543
            div7 |  -.1368482   .0992912    -1.38   0.168    -.3314554    .0577591
            div8 |   -.385249   .1234023    -3.12   0.002    -.6271131   -.1433849
            div9 |  -.3329298   .1175007    -2.83   0.005     -.563227   -.1026326
elevat_range_msa |   -.025972    .034827    -0.75   0.456    -.0942317    .0422878
  ruggedness_msa |   6.686251   2.055938     3.25   0.001     2.656687    10.71582
      heating_dd |  -.0148676   .0026431    -5.63   0.000     -.020048   -.0096872
      cooling_dd |  -.0224467     .00543    -4.13   0.000    -.0330893   -.0118042
          sprawl |   .0013096   .0018339     0.71   0.475    -.0022847    .0049039
        _Iyear_2 |   .3942304   .0307774    12.81   0.000     .3339079     .454553
        _Iyear_3 |   .6366666   .0308063    20.67   0.000     .5762873    .6970458
           _cons |   5.330718   .4859032    10.97   0.000     4.378365    6.283071
----------------------------------------------------------------------------------
Endogenous: l_ln
Exogenous:  l_pop div2 div3 div4 div5 div6 div7 div8 div9 elevat_range_msa
            ruggedness_msa heating_dd cooling_dd sprawl _Iyear_2 _Iyear_3
            l_rail1898 l_hwy1947 l_pix1835

.                 
.                 xi: genqreg l_vmt l_ln,  q(10) proneness(l_pop div2 div3 div4 div5 
> div6 div7 div8 div9 elevat_range_msa ruggedness_msa heating_dd cooling_dd sprawl i.
> year) instruments(l_rail1898 l_hwy1947 l_pix1835) optimize(grid) grid1(0.1(0.01)2) 
>                                                
i.year            _Iyear_1-3          (_Iyear_1 for year==_1983 omitted)
Grid-search optimization
Number of grid points: 191
 
Generalized Quantile Regression (GQR)
     Observations:                684
 
------------------------------------------------------------------------------
       l_vmt | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
        l_ln |       1.21   .1142192    10.59   0.000     .9861346    1.433865
------------------------------------------------------------------------------
Excluded intstruments: l_rail1898 l_hwy1947 l_pix1835
Proneness variables: l_pop div2 div3 div4 div5 div6 div7 div8 div9 elevat_range_msa r
> uggedness_msa heating_dd cooling_dd sprawl _Iyear_2 _Iyear_3

.                 xi: genqreg l_vmt l_ln,  q(25) proneness(l_pop div2 div3 div4 div5 
> div6 div7 div8 div9 elevat_range_msa ruggedness_msa heating_dd cooling_dd sprawl i.
> year) instruments(l_rail1898 l_hwy1947 l_pix1835) optimize(grid) grid1(0.1(0.01)2) 
>                                             
i.year            _Iyear_1-3          (_Iyear_1 for year==_1983 omitted)
Grid-search optimization
Number of grid points: 191
 
Generalized Quantile Regression (GQR)
     Observations:                684
 
------------------------------------------------------------------------------
       l_vmt | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
        l_ln |       1.22   .1373386     8.88   0.000     .9508213    1.489179
------------------------------------------------------------------------------
Excluded intstruments: l_rail1898 l_hwy1947 l_pix1835
Proneness variables: l_pop div2 div3 div4 div5 div6 div7 div8 div9 elevat_range_msa r
> uggedness_msa heating_dd cooling_dd sprawl _Iyear_2 _Iyear_3

.                 xi: genqreg l_vmt l_ln,  q(50) proneness(l_pop div2 div3 div4 div5 
> div6 div7 div8 div9 elevat_range_msa ruggedness_msa heating_dd cooling_dd sprawl i.
> year) instruments(l_rail1898 l_hwy1947 l_pix1835) optimize(grid) grid1(0.1(0.01)2) 
>                                                   
i.year            _Iyear_1-3          (_Iyear_1 for year==_1983 omitted)
Grid-search optimization
Number of grid points: 191
 
Generalized Quantile Regression (GQR)
     Observations:                684
 
------------------------------------------------------------------------------
       l_vmt | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
        l_ln |       1.04   .2284893     4.55   0.000     .5921692    1.487831
------------------------------------------------------------------------------
Excluded intstruments: l_rail1898 l_hwy1947 l_pix1835
Proneness variables: l_pop div2 div3 div4 div5 div6 div7 div8 div9 elevat_range_msa r
> uggedness_msa heating_dd cooling_dd sprawl _Iyear_2 _Iyear_3

.                 xi: genqreg l_vmt l_ln,  q(75) proneness(l_pop div2 div3 div4 div5 
> div6 div7 div8 div9 elevat_range_msa ruggedness_msa heating_dd cooling_dd sprawl i.
> year) instruments(l_rail1898 l_hwy1947 l_pix1835) optimize(grid) grid1(0.1(0.01)2) 
>                                                
i.year            _Iyear_1-3          (_Iyear_1 for year==_1983 omitted)
Grid-search optimization
Number of grid points: 191
 
Generalized Quantile Regression (GQR)
     Observations:                684
 
------------------------------------------------------------------------------
       l_vmt | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
        l_ln |       .865   .0633707    13.65   0.000     .7407957    .9892043
------------------------------------------------------------------------------
Excluded intstruments: l_rail1898 l_hwy1947 l_pix1835
Proneness variables: l_pop div2 div3 div4 div5 div6 div7 div8 div9 elevat_range_msa r
> uggedness_msa heating_dd cooling_dd sprawl _Iyear_2 _Iyear_3
    Note: Alternative solutions exist. See e(solutions).

.                 xi: genqreg l_vmt l_ln,  q(90) proneness(l_pop div2 div3 div4 div5 
> div6 div7 div8 div9 elevat_range_msa ruggedness_msa heating_dd cooling_dd sprawl i.
> year) instruments(l_rail1898 l_hwy1947 l_pix1835) optimize(grid) grid1(0.1(0.01)2) 
>                                             
i.year            _Iyear_1-3          (_Iyear_1 for year==_1983 omitted)
Grid-search optimization
Number of grid points: 191
 
Generalized Quantile Regression (GQR)
     Observations:                684
 
------------------------------------------------------------------------------
       l_vmt | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
        l_ln |        .85   .1829752     4.65   0.000     .4913753    1.208625
------------------------------------------------------------------------------
Excluded intstruments: l_rail1898 l_hwy1947 l_pix1835
Proneness variables: l_pop div2 div3 div4 div5 div6 div7 div8 div9 elevat_range_msa r
> uggedness_msa heating_dd cooling_dd sprawl _Iyear_2 _Iyear_3

. 
. 
. 
. * Model 4 *     
. 
.                 xi: ivregress 2sls l_vmt l_pop S_somecollege l_mean_income S_poor S
> _manuf div2 div3 div4 div5 div6 div7 div8 div9 elevat_range_msa ruggedness_msa heat
> ing_dd cooling_dd sprawl i.year (l_ln = l_rail1898 l_hwy1947 l_pix1835), robust    
>                                
i.year            _Iyear_1-3          (_Iyear_1 for year==_1983 omitted)

Instrumental variables 2SLS regression            Number of obs   =        684
                                                  Wald chi2(21)   =   13371.40
                                                  Prob > chi2     =     0.0000
                                                  R-squared       =     0.9488
                                                  Root MSE        =     .30994

----------------------------------------------------------------------------------
                 |               Robust
           l_vmt | Coefficient  std. err.      z    P>|z|     [95% conf. interval]
-----------------+----------------------------------------------------------------
            l_ln |   1.008793   .0774837    13.02   0.000     .8569279    1.160659
           l_pop |   .3435467   .0616805     5.57   0.000     .2226551    .4644382
   S_somecollege |   1.049689   .2239771     4.69   0.000     .6107024    1.488676
   l_mean_income |  -.7355972   .2101318    -3.50   0.000    -1.147448   -.3237464
          S_poor |  -.5171109   .3527822    -1.47   0.143    -1.208551    .1743296
         S_manuf |    .524865   .2536321     2.07   0.039     .0277552    1.021975
            div2 |  -.1236505   .0700403    -1.77   0.077    -.2609269     .013626
            div3 |   .1073522   .0852717     1.26   0.208    -.0597772    .2744816
            div4 |  -.0412027   .0921461    -0.45   0.655    -.2218056    .1394003
            div5 |  -.0848637   .0872127    -0.97   0.331    -.2557975      .08607
            div6 |  -.1791086   .0977478    -1.83   0.067    -.3706909    .0124736
            div7 |  -.0732386   .1063821    -0.69   0.491    -.2817437    .1352665
            div8 |  -.3063858    .118339    -2.59   0.010     -.538326   -.0744455
            div9 |  -.2378768   .1118742    -2.13   0.033    -.4571463   -.0186073
elevat_range_msa |  -.0512695    .031601    -1.62   0.105    -.1132063    .0106674
  ruggedness_msa |   6.790079   1.922884     3.53   0.000     3.021296    10.55886
      heating_dd |  -.0136714   .0025939    -5.27   0.000    -.0187552   -.0085875
      cooling_dd |  -.0184901   .0056436    -3.28   0.001    -.0295514   -.0074288
          sprawl |   .0021733   .0018532     1.17   0.241     -.001459    .0058055
        _Iyear_2 |   .2757887   .0491071     5.62   0.000     .1795405    .3720369
        _Iyear_3 |   .3919424   .0882701     4.44   0.000     .2189363    .5649486
           _cons |   11.72303   1.932125     6.07   0.000     7.936138    15.50993
----------------------------------------------------------------------------------
Endogenous: l_ln
Exogenous:  l_pop S_somecollege l_mean_income S_poor S_manuf div2 div3 div4 div5
            div6 div7 div8 div9 elevat_range_msa ruggedness_msa heating_dd
            cooling_dd sprawl _Iyear_2 _Iyear_3 l_rail1898 l_hwy1947 l_pix1835

.                 
.                 xi: genqreg l_vmt l_ln,  q(10) proneness(l_pop S_somecollege l_mean
> _income S_poor S_manuf div2 div3 div4 div5 div6 div7 div8 div9 elevat_range_msa rug
> gedness_msa heating_dd cooling_dd sprawl i.year) instruments(l_rail1898 l_hwy1947 l
> _pix1835) optimize(grid) grid1(0.1(0.01)2)             
i.year            _Iyear_1-3          (_Iyear_1 for year==_1983 omitted)
Grid-search optimization
Number of grid points: 191
 
Generalized Quantile Regression (GQR)
     Observations:                684
 
------------------------------------------------------------------------------
       l_vmt | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
        l_ln |      1.275   .1444488     8.83   0.000     .9918855    1.558115
------------------------------------------------------------------------------
Excluded intstruments: l_rail1898 l_hwy1947 l_pix1835
Proneness variables: l_pop S_somecollege l_mean_income S_poor S_manuf div2 div3 div4 
> div5 div6 div7 div8 div9 elevat_range_msa ruggedness_msa heating_dd cooling_dd spra
> wl _Iyear_2 _Iyear_3
    Note: Alternative solutions exist. See e(solutions).

.                 xi: genqreg l_vmt l_ln,  q(25) proneness(l_pop S_somecollege l_mean
> _income S_poor S_manuf div2 div3 div4 div5 div6 div7 div8 div9 elevat_range_msa rug
> gedness_msa heating_dd cooling_dd sprawl i.year) instruments(l_rail1898 l_hwy1947 l
> _pix1835)  optimize(grid) grid1(0.1(0.01)2)        
i.year            _Iyear_1-3          (_Iyear_1 for year==_1983 omitted)
Grid-search optimization
Number of grid points: 191
 
Generalized Quantile Regression (GQR)
     Observations:                684
 
------------------------------------------------------------------------------
       l_vmt | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
        l_ln |       1.22   .1375115     8.87   0.000     .9504824    1.489518
------------------------------------------------------------------------------
Excluded intstruments: l_rail1898 l_hwy1947 l_pix1835
Proneness variables: l_pop S_somecollege l_mean_income S_poor S_manuf div2 div3 div4 
> div5 div6 div7 div8 div9 elevat_range_msa ruggedness_msa heating_dd cooling_dd spra
> wl _Iyear_2 _Iyear_3

.                 xi: genqreg l_vmt l_ln,  q(50) proneness(l_pop S_somecollege l_mean
> _income S_poor S_manuf div2 div3 div4 div5 div6 div7 div8 div9 elevat_range_msa rug
> gedness_msa heating_dd cooling_dd sprawl i.year) instruments(l_rail1898 l_hwy1947 l
> _pix1835)  optimize(grid) grid1(0.1(0.01)2)      
i.year            _Iyear_1-3          (_Iyear_1 for year==_1983 omitted)
Grid-search optimization
Number of grid points: 191
 
Generalized Quantile Regression (GQR)
     Observations:                684
 
------------------------------------------------------------------------------
       l_vmt | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
        l_ln |       1.04   .2384578     4.36   0.000     .5726312    1.507369
------------------------------------------------------------------------------
Excluded intstruments: l_rail1898 l_hwy1947 l_pix1835
Proneness variables: l_pop S_somecollege l_mean_income S_poor S_manuf div2 div3 div4 
> div5 div6 div7 div8 div9 elevat_range_msa ruggedness_msa heating_dd cooling_dd spra
> wl _Iyear_2 _Iyear_3

.                 xi: genqreg l_vmt l_ln,  q(75) proneness(l_pop S_somecollege l_mean
> _income S_poor S_manuf div2 div3 div4 div5 div6 div7 div8 div9 elevat_range_msa rug
> gedness_msa heating_dd cooling_dd sprawl i.year) instruments(l_rail1898 l_hwy1947 l
> _pix1835)  optimize(grid) grid1(0.1(0.01)2)      
i.year            _Iyear_1-3          (_Iyear_1 for year==_1983 omitted)
Grid-search optimization
Number of grid points: 191
 
Generalized Quantile Regression (GQR)
     Observations:                684
 
------------------------------------------------------------------------------
       l_vmt | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
        l_ln |        .85    .073886    11.50   0.000      .705186     .994814
------------------------------------------------------------------------------
Excluded intstruments: l_rail1898 l_hwy1947 l_pix1835
Proneness variables: l_pop S_somecollege l_mean_income S_poor S_manuf div2 div3 div4 
> div5 div6 div7 div8 div9 elevat_range_msa ruggedness_msa heating_dd cooling_dd spra
> wl _Iyear_2 _Iyear_3

.                 xi: genqreg l_vmt l_ln,  q(90) proneness(l_pop S_somecollege l_mean
> _income S_poor S_manuf div2 div3 div4 div5 div6 div7 div8 div9 elevat_range_msa rug
> gedness_msa heating_dd cooling_dd sprawl i.year) instruments(l_rail1898 l_hwy1947 l
> _pix1835) optimize(grid) grid1(0.1(0.01)2)                     
i.year            _Iyear_1-3          (_Iyear_1 for year==_1983 omitted)
Grid-search optimization
Number of grid points: 191
 
Generalized Quantile Regression (GQR)
     Observations:                684
 
------------------------------------------------------------------------------
       l_vmt | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
        l_ln |        .87   .1127003     7.72   0.000     .6491115    1.090888
------------------------------------------------------------------------------
Excluded intstruments: l_rail1898 l_hwy1947 l_pix1835
Proneness variables: l_pop S_somecollege l_mean_income S_poor S_manuf div2 div3 div4 
> div5 div6 div7 div8 div9 elevat_range_msa ruggedness_msa heating_dd cooling_dd spra
> wl _Iyear_2 _Iyear_3

. 
. 
. 
. * Model 5 *     
. 
.                 xi: ivregress 2sls l_vmt l_pop l_pop80 l_pop70 l_pop60 l_pop50 l_po
> p40 l_pop30 l_pop20 S_somecollege l_mean_income S_poor S_manuf div2 div3 div4 div5 
> div6 div7 div8 div9 elevat_range_msa ruggedness_msa heating_dd cooling_dd sprawl i.
> year (l_ln = l_rail1898 l_hwy1947 l_pix1835), robust                               
>     
i.year            _Iyear_1-3          (_Iyear_1 for year==_1983 omitted)

Instrumental variables 2SLS regression            Number of obs   =        684
                                                  Wald chi2(28)   =   14112.87
                                                  Prob > chi2     =     0.0000
                                                  R-squared       =     0.9497
                                                  Root MSE        =     .30692

----------------------------------------------------------------------------------
                 |               Robust
           l_vmt | Coefficient  std. err.      z    P>|z|     [95% conf. interval]
-----------------+----------------------------------------------------------------
            l_ln |   1.039813   .0844881    12.31   0.000     .8742196    1.205407
           l_pop |    .233254   .1309338     1.78   0.075    -.0233716    .4898796
         l_pop80 |    .674398   .2878259     2.34   0.019     .1102695    1.238526
         l_pop70 |  -.1888125   .3516301    -0.54   0.591    -.8779949    .5003699
         l_pop60 |  -.5019758   .2773509    -1.81   0.070    -1.045574     .041622
         l_pop50 |  -.0334606    .175969    -0.19   0.849    -.3783534    .3114323
         l_pop40 |   .0054055   .1924353     0.03   0.978    -.3717607    .3825717
         l_pop30 |   .1401975   .1646682     0.85   0.395    -.1825462    .4629411
         l_pop20 |  -.0068555   .0806719    -0.08   0.932    -.1649695    .1512585
   S_somecollege |   .5910127   .2847117     2.08   0.038     .0329881    1.149037
   l_mean_income |  -.5940658   .2103193    -2.82   0.005    -1.006284   -.1818476
          S_poor |  -.3219207    .355494    -0.91   0.365    -1.018676    .3748347
         S_manuf |   .4799153   .2464645     1.95   0.052    -.0031463    .9629768
            div2 |  -.1404319   .0718841    -1.95   0.051    -.2813222    .0004583
            div3 |   .0770117   .0899273     0.86   0.392    -.0992425     .253266
            div4 |  -.0409546   .0923378    -0.44   0.657    -.2219333    .1400241
            div5 |  -.1245138   .0911048    -1.37   0.172     -.303076    .0540484
            div6 |    -.20477   .1003513    -2.04   0.041     -.401455    -.008085
            div7 |  -.0685621   .1081257    -0.63   0.526    -.2804846    .1433604
            div8 |    -.38036   .1405709    -2.71   0.007    -.6558738   -.1048461
            div9 |   -.268726   .1336834    -2.01   0.044    -.5307407   -.0067114
elevat_range_msa |  -.0580455   .0311163    -1.87   0.062    -.1190323    .0029414
  ruggedness_msa |   5.103778   1.925562     2.65   0.008     1.329746     8.87781
      heating_dd |  -.0157404   .0027467    -5.73   0.000    -.0211238    -.010357
      cooling_dd |  -.0286064   .0063494    -4.51   0.000    -.0410509   -.0161619
          sprawl |    .001145   .0020114     0.57   0.569    -.0027973    .0050873
        _Iyear_2 |     .34237   .0543143     6.30   0.000      .235916    .4488241
        _Iyear_3 |   .5292628   .1020702     5.19   0.000     .3292089    .7293167
           _cons |   10.73986   1.923733     5.58   0.000     6.969411    14.51031
----------------------------------------------------------------------------------
Endogenous: l_ln
Exogenous:  l_pop l_pop80 l_pop70 l_pop60 l_pop50 l_pop40 l_pop30 l_pop20
            S_somecollege l_mean_income S_poor S_manuf div2 div3 div4 div5 div6
            div7 div8 div9 elevat_range_msa ruggedness_msa heating_dd cooling_dd
            sprawl _Iyear_2 _Iyear_3 l_rail1898 l_hwy1947 l_pix1835

.                 
.                 xi: genqreg l_vmt l_ln,  q(10) proneness(l_pop l_pop80 l_pop70 l_po
> p60 l_pop50 l_pop40 l_pop30 l_pop20 S_somecollege l_mean_income S_poor S_manuf div2
>  div3 div4 div5 div6 div7 div8 div9 elevat_range_msa ruggedness_msa heating_dd cool
> ing_dd sprawl i.year) instruments(l_rail1898 l_hwy1947 l_pix1835) optimize(grid) gr
> id1(0.1(0.01)2)       
i.year            _Iyear_1-3          (_Iyear_1 for year==_1983 omitted)
Grid-search optimization
Number of grid points: 191
 
Generalized Quantile Regression (GQR)
     Observations:                684
 
------------------------------------------------------------------------------
       l_vmt | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
        l_ln |       1.21   .1209169    10.01   0.000     .9730072    1.446993
------------------------------------------------------------------------------
Excluded intstruments: l_rail1898 l_hwy1947 l_pix1835
Proneness variables: l_pop l_pop80 l_pop70 l_pop60 l_pop50 l_pop40 l_pop30 l_pop20 S_
> somecollege l_mean_income S_poor S_manuf div2 div3 div4 div5 div6 div7 div8 div9 el
> evat_range_msa ruggedness_msa heating_dd cooling_dd sprawl _Iyear_2 _Iyear_3

.                 xi: genqreg l_vmt l_ln,  q(25) proneness(l_pop l_pop80 l_pop70 l_po
> p60 l_pop50 l_pop40 l_pop30 l_pop20 S_somecollege l_mean_income S_poor S_manuf div2
>  div3 div4 div5 div6 div7 div8 div9 elevat_range_msa ruggedness_msa heating_dd cool
> ing_dd sprawl i.year) instruments(l_rail1898 l_hwy1947 l_pix1835) optimize(grid) gr
> id1(0.1(0.01)2)     
i.year            _Iyear_1-3          (_Iyear_1 for year==_1983 omitted)
Grid-search optimization
Number of grid points: 191
 
Generalized Quantile Regression (GQR)
     Observations:                684
 
------------------------------------------------------------------------------
       l_vmt | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
        l_ln |       1.22   .1433084     8.51   0.000     .9391208    1.500879
------------------------------------------------------------------------------
Excluded intstruments: l_rail1898 l_hwy1947 l_pix1835
Proneness variables: l_pop l_pop80 l_pop70 l_pop60 l_pop50 l_pop40 l_pop30 l_pop20 S_
> somecollege l_mean_income S_poor S_manuf div2 div3 div4 div5 div6 div7 div8 div9 el
> evat_range_msa ruggedness_msa heating_dd cooling_dd sprawl _Iyear_2 _Iyear_3

.                 xi: genqreg l_vmt l_ln,  q(50) proneness(l_pop l_pop80 l_pop70 l_po
> p60 l_pop50 l_pop40 l_pop30 l_pop20 S_somecollege l_mean_income S_poor S_manuf div2
>  div3 div4 div5 div6 div7 div8 div9 elevat_range_msa ruggedness_msa heating_dd cool
> ing_dd sprawl i.year) instruments(l_rail1898 l_hwy1947 l_pix1835) optimize(grid) gr
> id1(0.1(0.01)2)   
i.year            _Iyear_1-3          (_Iyear_1 for year==_1983 omitted)
Grid-search optimization
Number of grid points: 191
 
Generalized Quantile Regression (GQR)
     Observations:                684
 
------------------------------------------------------------------------------
       l_vmt | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
        l_ln |       1.02   .1857281     5.49   0.000     .6559796     1.38402
------------------------------------------------------------------------------
Excluded intstruments: l_rail1898 l_hwy1947 l_pix1835
Proneness variables: l_pop l_pop80 l_pop70 l_pop60 l_pop50 l_pop40 l_pop30 l_pop20 S_
> somecollege l_mean_income S_poor S_manuf div2 div3 div4 div5 div6 div7 div8 div9 el
> evat_range_msa ruggedness_msa heating_dd cooling_dd sprawl _Iyear_2 _Iyear_3

.                 xi: genqreg l_vmt l_ln,  q(75) proneness(l_pop l_pop80 l_pop70 l_po
> p60 l_pop50 l_pop40 l_pop30 l_pop20 S_somecollege l_mean_income S_poor S_manuf div2
>  div3 div4 div5 div6 div7 div8 div9 elevat_range_msa ruggedness_msa heating_dd cool
> ing_dd sprawl i.year) instruments(l_rail1898 l_hwy1947 l_pix1835) optimize(grid) gr
> id1(0.1(0.01)2)   
i.year            _Iyear_1-3          (_Iyear_1 for year==_1983 omitted)
Grid-search optimization
Number of grid points: 191
 
Generalized Quantile Regression (GQR)
     Observations:                684
 
------------------------------------------------------------------------------
       l_vmt | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
        l_ln |        .85   .0672382    12.64   0.000     .7182156    .9817844
------------------------------------------------------------------------------
Excluded intstruments: l_rail1898 l_hwy1947 l_pix1835
Proneness variables: l_pop l_pop80 l_pop70 l_pop60 l_pop50 l_pop40 l_pop30 l_pop20 S_
> somecollege l_mean_income S_poor S_manuf div2 div3 div4 div5 div6 div7 div8 div9 el
> evat_range_msa ruggedness_msa heating_dd cooling_dd sprawl _Iyear_2 _Iyear_3

.                 xi: genqreg l_vmt l_ln,  q(90) proneness(l_pop l_pop80 l_pop70 l_po
> p60 l_pop50 l_pop40 l_pop30 l_pop20 S_somecollege l_mean_income S_poor S_manuf div2
>  div3 div4 div5 div6 div7 div8 div9 elevat_range_msa ruggedness_msa heating_dd cool
> ing_dd sprawl i.year) instruments(l_rail1898 l_hwy1947 l_pix1835) optimize(grid) gr
> id1(0.1(0.01)2)     
i.year            _Iyear_1-3          (_Iyear_1 for year==_1983 omitted)
Grid-search optimization
Number of grid points: 191
 
Generalized Quantile Regression (GQR)
     Observations:                684
 
------------------------------------------------------------------------------
       l_vmt | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
        l_ln |        .78   .1546364     5.04   0.000     .4769182    1.083082
------------------------------------------------------------------------------
Excluded intstruments: l_rail1898 l_hwy1947 l_pix1835
Proneness variables: l_pop l_pop80 l_pop70 l_pop60 l_pop50 l_pop40 l_pop30 l_pop20 S_
> somecollege l_mean_income S_poor S_manuf div2 div3 div4 div5 div6 div7 div8 div9 el
> evat_range_msa ruggedness_msa heating_dd cooling_dd sprawl _Iyear_2 _Iyear_3

. 
.                 
.                 
.         
.         
.         
. * Second Panel *        
.                 
.                 local Inst "l_hwy1947"

.                 capture drop l_vmt l_ln

.                 gen l_vmt = l_vmt_IH  

.                 gen l_ln  = l_ln_km_IH 

. 
. 
. 
. * Model 1 *     
. 
.                 xi: ivregress 2sls l_vmt i.year (l_ln =  l_hwy1947 ), robust      
i.year            _Iyear_1-3          (_Iyear_1 for year==_1983 omitted)

Instrumental variables 2SLS regression            Number of obs   =        684
                                                  Wald chi2(3)    =    2626.59
                                                  Prob > chi2     =     0.0000
                                                  R-squared       =     0.8726
                                                  Root MSE        =     .48861

------------------------------------------------------------------------------
             |               Robust
       l_vmt | Coefficient  std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
        l_ln |   1.333793    .028953    46.07   0.000     1.277046     1.39054
    _Iyear_2 |   .3973409   .0473145     8.40   0.000     .3046062    .4900756
    _Iyear_3 |   .6626131   .0467988    14.16   0.000     .5708892     .754337
       _cons |   6.164348   .1888629    32.64   0.000     5.794184    6.534513
------------------------------------------------------------------------------
Endogenous: l_ln
Exogenous:  _Iyear_2 _Iyear_3 l_hwy1947

. 
.                 xi: genqreg l_vmt l_ln,  q(10) proneness(i.year) instruments( l_hwy
> 1947 )  optimize(grid) grid1(0.1(0.01)2)     
i.year            _Iyear_1-3          (_Iyear_1 for year==_1983 omitted)
Grid-search optimization
Number of grid points: 191
 
Generalized Quantile Regression (GQR)
     Observations:                684
 
------------------------------------------------------------------------------
       l_vmt | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
        l_ln |      1.435   .1966223     7.30   0.000     1.049627    1.820373
------------------------------------------------------------------------------
Excluded intstruments: l_hwy1947
Proneness variables: _Iyear_2 _Iyear_3
    Note: Alternative solutions exist. See e(solutions).

.                 xi: genqreg l_vmt l_ln,  q(25) proneness(i.year) instruments( l_hwy
> 1947 )  optimize(grid) grid1(0.1(0.01)2)          
i.year            _Iyear_1-3          (_Iyear_1 for year==_1983 omitted)
Grid-search optimization
Number of grid points: 191
 
Generalized Quantile Regression (GQR)
     Observations:                684
 
------------------------------------------------------------------------------
       l_vmt | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
        l_ln |      1.395   .0729195    19.13   0.000      1.25208     1.53792
------------------------------------------------------------------------------
Excluded intstruments: l_hwy1947
Proneness variables: _Iyear_2 _Iyear_3
    Note: Alternative solutions exist. See e(solutions).

.                 xi: genqreg l_vmt l_ln,  q(50) proneness(i.year) instruments( l_hwy
> 1947 )  optimize(grid) grid1(0.1(0.01)2)            
i.year            _Iyear_1-3          (_Iyear_1 for year==_1983 omitted)
Grid-search optimization
Number of grid points: 191
 
Generalized Quantile Regression (GQR)
     Observations:                684
 
------------------------------------------------------------------------------
       l_vmt | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
        l_ln |       1.35   .0814246    16.58   0.000     1.190411    1.509589
------------------------------------------------------------------------------
Excluded intstruments: l_hwy1947
Proneness variables: _Iyear_2 _Iyear_3

.                 xi: genqreg l_vmt l_ln,  q(75) proneness(i.year) instruments( l_hwy
> 1947 ) optimize(grid) grid1(0.1(0.01)2)   
i.year            _Iyear_1-3          (_Iyear_1 for year==_1983 omitted)
Grid-search optimization
Number of grid points: 191
 
Generalized Quantile Regression (GQR)
     Observations:                684
 
------------------------------------------------------------------------------
       l_vmt | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
        l_ln |       1.24   .0945697    13.11   0.000     1.054647    1.425353
------------------------------------------------------------------------------
Excluded intstruments: l_hwy1947
Proneness variables: _Iyear_2 _Iyear_3

.                 xi: genqreg l_vmt l_ln,  q(90) proneness(i.year) instruments( l_hwy
> 1947 ) optimize(grid) grid1(0.1(0.01)2)      
i.year            _Iyear_1-3          (_Iyear_1 for year==_1983 omitted)
Grid-search optimization
Number of grid points: 191
 
Generalized Quantile Regression (GQR)
     Observations:                684
 
------------------------------------------------------------------------------
       l_vmt | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
        l_ln |       1.21   .6770962     1.79   0.074    -.1170841    2.537084
------------------------------------------------------------------------------
Excluded intstruments: l_hwy1947
Proneness variables: _Iyear_2 _Iyear_3

. 
. 
. 
. * Model 2 *     
. 
.                 xi: ivregress 2sls l_vmt l_pop i.year  (l_ln =  l_hwy1947 ), robust
>                      
i.year            _Iyear_1-3          (_Iyear_1 for year==_1983 omitted)

Instrumental variables 2SLS regression            Number of obs   =        684
                                                  Wald chi2(4)    =    7210.35
                                                  Prob > chi2     =     0.0000
                                                  R-squared       =     0.9295
                                                  Root MSE        =     .36354

------------------------------------------------------------------------------
             |               Robust
       l_vmt | Coefficient  std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
        l_ln |   .9999055    .069237    14.44   0.000     .8642035    1.135607
       l_pop |   .3519581   .0516779     6.81   0.000     .2506713    .4532449
    _Iyear_2 |   .3928344   .0347872    11.29   0.000     .3246528    .4610161
    _Iyear_3 |     .63034   .0346843    18.17   0.000     .5623599      .69832
       _cons |   3.855266   .2554794    15.09   0.000     3.354536    4.355996
------------------------------------------------------------------------------
Endogenous: l_ln
Exogenous:  l_pop _Iyear_2 _Iyear_3 l_hwy1947

. 
.                 xi: genqreg l_vmt l_ln,  q(10) proneness(l_pop i.year) instruments(
>  l_hwy1947 ) optimize(grid) grid1(0.1(0.01)2)
i.year            _Iyear_1-3          (_Iyear_1 for year==_1983 omitted)
Grid-search optimization
Number of grid points: 191
 
Generalized Quantile Regression (GQR)
     Observations:                684
 
------------------------------------------------------------------------------
       l_vmt | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
        l_ln |        .97   .1449743     6.69   0.000     .6858557    1.254144
------------------------------------------------------------------------------
Excluded intstruments: l_hwy1947
Proneness variables: l_pop _Iyear_2 _Iyear_3

.                 xi: genqreg l_vmt l_ln,  q(25) proneness(l_pop i.year) instruments(
>  l_hwy1947 ) optimize(grid) grid1(0.1(0.01)2)    
i.year            _Iyear_1-3          (_Iyear_1 for year==_1983 omitted)
Grid-search optimization
Number of grid points: 191
 
Generalized Quantile Regression (GQR)
     Observations:                684
 
------------------------------------------------------------------------------
       l_vmt | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
        l_ln |       1.11   1.136683     0.98   0.329    -1.117858    3.337858
------------------------------------------------------------------------------
Excluded intstruments: l_hwy1947
Proneness variables: l_pop _Iyear_2 _Iyear_3

.                 xi: genqreg l_vmt l_ln,  q(50) proneness(l_pop i.year) instruments(
>  l_hwy1947 ) optimize(grid) grid1(0.1(0.01)2)     
i.year            _Iyear_1-3          (_Iyear_1 for year==_1983 omitted)
Grid-search optimization
Number of grid points: 191
 
Generalized Quantile Regression (GQR)
     Observations:                684
 
------------------------------------------------------------------------------
       l_vmt | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
        l_ln |       1.14   .1825371     6.25   0.000     .7822338    1.497766
------------------------------------------------------------------------------
Excluded intstruments: l_hwy1947
Proneness variables: l_pop _Iyear_2 _Iyear_3

.                 xi: genqreg l_vmt l_ln,  q(75) proneness(l_pop i.year) instruments(
>  l_hwy1947 ) optimize(grid) grid1(0.1(0.01)2)     
i.year            _Iyear_1-3          (_Iyear_1 for year==_1983 omitted)
Grid-search optimization
Number of grid points: 191
 
Generalized Quantile Regression (GQR)
     Observations:                684
 
------------------------------------------------------------------------------
       l_vmt | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
        l_ln |        .95    .129646     7.33   0.000     .6958985    1.204102
------------------------------------------------------------------------------
Excluded intstruments: l_hwy1947
Proneness variables: l_pop _Iyear_2 _Iyear_3

.                 xi: genqreg l_vmt l_ln,  q(90) proneness(l_pop i.year) instruments(
>  l_hwy1947 ) optimize(grid) grid1(0.1(0.01)2) 
i.year            _Iyear_1-3          (_Iyear_1 for year==_1983 omitted)
Grid-search optimization
Number of grid points: 191
 
Generalized Quantile Regression (GQR)
     Observations:                684
 
------------------------------------------------------------------------------
       l_vmt | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
        l_ln |       .575   .7442338     0.77   0.440    -.8836715    2.033672
------------------------------------------------------------------------------
Excluded intstruments: l_hwy1947
Proneness variables: l_pop _Iyear_2 _Iyear_3
    Note: Alternative solutions exist. See e(solutions).

. 
. 
. * Model 3 *     
. 
.                 xi: ivregress 2sls l_vmt l_pop div2 div3 div4 div5 div6 div7 div8 d
> iv9 elevat_range_msa ruggedness_msa heating_dd cooling_dd sprawl i.year  (l_ln =  l
> _hwy1947 ), robust                                
i.year            _Iyear_1-3          (_Iyear_1 for year==_1983 omitted)

Instrumental variables 2SLS regression            Number of obs   =        684
                                                  Wald chi2(17)   =   11308.55
                                                  Prob > chi2     =     0.0000
                                                  R-squared       =     0.9398
                                                  Root MSE        =     .33594

----------------------------------------------------------------------------------
                 |               Robust
           l_vmt | Coefficient  std. err.      z    P>|z|     [95% conf. interval]
-----------------+----------------------------------------------------------------
            l_ln |   1.102506   .0781378    14.11   0.000     .9493588    1.255653
           l_pop |   .2431158   .0630777     3.85   0.000     .1194858    .3667458
            div2 |  -.1884822   .0687676    -2.74   0.006    -.3232642   -.0537002
            div3 |   .0124763   .0830866     0.15   0.881    -.1503704     .175323
            div4 |  -.0912973   .0976838    -0.93   0.350    -.2827539    .1001594
            div5 |   -.104381   .0935216    -1.12   0.264    -.2876801     .078918
            div6 |   -.220692   .0982829    -2.25   0.025     -.413323    -.028061
            div7 |  -.1797895   .1051725    -1.71   0.087    -.3859239    .0263449
            div8 |  -.4647331   .1352422    -3.44   0.001     -.729803   -.1996633
            div9 |  -.3802147   .1261847    -3.01   0.003    -.6275323   -.1328972
elevat_range_msa |   -.023727   .0366888    -0.65   0.518    -.0956357    .0481817
  ruggedness_msa |   6.894112   2.159781     3.19   0.001     2.661018     11.1272
      heating_dd |  -.0157042   .0028184    -5.57   0.000    -.0212282   -.0101802
      cooling_dd |  -.0237658   .0058227    -4.08   0.000    -.0351781   -.0123535
          sprawl |   .0003782   .0020399     0.19   0.853    -.0036199    .0043763
        _Iyear_2 |   .3940516   .0320932    12.28   0.000       .33115    .4569531
        _Iyear_3 |   .6401744   .0321762    19.90   0.000     .5771102    .7032387
           _cons |   5.727713   .5365113    10.68   0.000      4.67617    6.779255
----------------------------------------------------------------------------------
Endogenous: l_ln
Exogenous:  l_pop div2 div3 div4 div5 div6 div7 div8 div9 elevat_range_msa
            ruggedness_msa heating_dd cooling_dd sprawl _Iyear_2 _Iyear_3
            l_hwy1947

.                 
.                 xi: genqreg l_vmt l_ln,  q(10) proneness(l_pop div2 div3 div4 div5 
> div6 div7 div8 div9 elevat_range_msa ruggedness_msa heating_dd cooling_dd sprawl i.
> year) instruments( l_hwy1947 ) optimize(grid) grid1(0.1(0.01)2) 
i.year            _Iyear_1-3          (_Iyear_1 for year==_1983 omitted)
Grid-search optimization
Number of grid points: 191
 
Generalized Quantile Regression (GQR)
     Observations:                684
 
------------------------------------------------------------------------------
       l_vmt | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
        l_ln |        1.3   .1583212     8.21   0.000     .9896961    1.610304
------------------------------------------------------------------------------
Excluded intstruments: l_hwy1947
Proneness variables: l_pop div2 div3 div4 div5 div6 div7 div8 div9 elevat_range_msa r
> uggedness_msa heating_dd cooling_dd sprawl _Iyear_2 _Iyear_3

.                 xi: genqreg l_vmt l_ln,  q(25) proneness(l_pop div2 div3 div4 div5 
> div6 div7 div8 div9 elevat_range_msa ruggedness_msa heating_dd cooling_dd sprawl i.
> year) instruments( l_hwy1947 )   optimize(grid) grid1(0.1(0.01)2)                  
>     
i.year            _Iyear_1-3          (_Iyear_1 for year==_1983 omitted)
Grid-search optimization
Number of grid points: 191
 
Generalized Quantile Regression (GQR)
     Observations:                684
 
------------------------------------------------------------------------------
       l_vmt | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
        l_ln |       1.25   .1278465     9.78   0.000     .9994255    1.500574
------------------------------------------------------------------------------
Excluded intstruments: l_hwy1947
Proneness variables: l_pop div2 div3 div4 div5 div6 div7 div8 div9 elevat_range_msa r
> uggedness_msa heating_dd cooling_dd sprawl _Iyear_2 _Iyear_3

.                 xi: genqreg l_vmt l_ln,  q(50) proneness(l_pop div2 div3 div4 div5 
> div6 div7 div8 div9 elevat_range_msa ruggedness_msa heating_dd cooling_dd sprawl i.
> year) instruments( l_hwy1947 ) optimize(grid) grid1(0.1(0.01)2)                    
>   
i.year            _Iyear_1-3          (_Iyear_1 for year==_1983 omitted)
Grid-search optimization
Number of grid points: 191
 
Generalized Quantile Regression (GQR)
     Observations:                684
 
------------------------------------------------------------------------------
       l_vmt | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
        l_ln |        1.2   .4576896     2.62   0.009     .3029448    2.097055
------------------------------------------------------------------------------
Excluded intstruments: l_hwy1947
Proneness variables: l_pop div2 div3 div4 div5 div6 div7 div8 div9 elevat_range_msa r
> uggedness_msa heating_dd cooling_dd sprawl _Iyear_2 _Iyear_3

.                 xi: genqreg l_vmt l_ln,  q(75) proneness(l_pop div2 div3 div4 div5 
> div6 div7 div8 div9 elevat_range_msa ruggedness_msa heating_dd cooling_dd sprawl i.
> year) instruments( l_hwy1947 )  optimize(grid) grid1(0.1(0.01)2)                  
i.year            _Iyear_1-3          (_Iyear_1 for year==_1983 omitted)
Grid-search optimization
Number of grid points: 191
 
Generalized Quantile Regression (GQR)
     Observations:                684
 
------------------------------------------------------------------------------
       l_vmt | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
        l_ln |        .93   .1723622     5.40   0.000     .5921762    1.267824
------------------------------------------------------------------------------
Excluded intstruments: l_hwy1947
Proneness variables: l_pop div2 div3 div4 div5 div6 div7 div8 div9 elevat_range_msa r
> uggedness_msa heating_dd cooling_dd sprawl _Iyear_2 _Iyear_3

.                 xi: genqreg l_vmt l_ln,  q(90) proneness(l_pop div2 div3 div4 div5 
> div6 div7 div8 div9 elevat_range_msa ruggedness_msa heating_dd cooling_dd sprawl i.
> year) instruments( l_hwy1947 ) optimize(grid) grid1(0.1(0.01)2)     
i.year            _Iyear_1-3          (_Iyear_1 for year==_1983 omitted)
Grid-search optimization
Number of grid points: 191
 
Generalized Quantile Regression (GQR)
     Observations:                684
 
------------------------------------------------------------------------------
       l_vmt | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
        l_ln |       .215   .6125598     0.35   0.726    -.9855952    1.415595
------------------------------------------------------------------------------
Excluded intstruments: l_hwy1947
Proneness variables: l_pop div2 div3 div4 div5 div6 div7 div8 div9 elevat_range_msa r
> uggedness_msa heating_dd cooling_dd sprawl _Iyear_2 _Iyear_3
    Note: Alternative solutions exist. See e(solutions).

. 
. 
. 
. * Model 4 *     
. 
.                 xi: ivregress 2sls l_vmt l_pop S_somecollege l_mean_income S_poor S
> _manuf div2 div3 div4 div5 div6 div7 div8 div9 elevat_range_msa ruggedness_msa heat
> ing_dd cooling_dd sprawl i.year (l_ln =  l_hwy1947 ), robust                       
>                
i.year            _Iyear_1-3          (_Iyear_1 for year==_1983 omitted)

Instrumental variables 2SLS regression            Number of obs   =        684
                                                  Wald chi2(21)   =   12430.52
                                                  Prob > chi2     =     0.0000
                                                  R-squared       =     0.9443
                                                  Root MSE        =     .32325

----------------------------------------------------------------------------------
                 |               Robust
           l_vmt | Coefficient  std. err.      z    P>|z|     [95% conf. interval]
-----------------+----------------------------------------------------------------
            l_ln |   1.081166   .0832774    12.98   0.000     .9179451    1.244386
           l_pop |   .2855688     .06766     4.22   0.000     .1529576      .41818
   S_somecollege |   1.048668   .2363577     4.44   0.000     .5854157    1.511921
   l_mean_income |   -.697196   .2194139    -3.18   0.001    -1.127239   -.2671525
          S_poor |  -.5043539   .3639244    -1.39   0.166    -1.217633    .2089248
         S_manuf |   .6832022   .2625661     2.60   0.009     .1685821    1.197822
            div2 |  -.1424039   .0764912    -1.86   0.063    -.2923239    .0075162
            div3 |   .0661042   .0955148     0.69   0.489    -.1211014    .2533099
            div4 |  -.0880574   .1027834    -0.86   0.392    -.2895091    .1133943
            div5 |  -.1154341     .09311    -1.24   0.215    -.2979263    .0670581
            div6 |  -.2268524   .1040005    -2.18   0.029    -.4306897   -.0230151
            div7 |  -.1161813   .1123186    -1.03   0.301    -.3363216     .103959
            div8 |  -.3773249   .1315204    -2.87   0.004    -.6351001   -.1195498
            div9 |  -.2754086   .1208684    -2.28   0.023    -.5123064   -.0385108
elevat_range_msa |  -.0496384    .033237    -1.49   0.135    -.1147817    .0155049
  ruggedness_msa |   7.096535   2.013841     3.52   0.000     3.149479    11.04359
      heating_dd |   -.014067   .0027911    -5.04   0.000    -.0195374   -.0085966
      cooling_dd |   -.018368   .0059643    -3.08   0.002    -.0300579   -.0066782
          sprawl |   .0011609   .0020537     0.57   0.572    -.0028644    .0051861
        _Iyear_2 |   .2852633   .0507936     5.62   0.000     .1857096     .384817
        _Iyear_3 |   .4129282   .0909311     4.54   0.000     .2347065    .5911498
           _cons |   11.66684   1.994238     5.85   0.000     7.758203    15.57547
----------------------------------------------------------------------------------
Endogenous: l_ln
Exogenous:  l_pop S_somecollege l_mean_income S_poor S_manuf div2 div3 div4 div5
            div6 div7 div8 div9 elevat_range_msa ruggedness_msa heating_dd
            cooling_dd sprawl _Iyear_2 _Iyear_3 l_hwy1947

.                 
.                 xi: genqreg l_vmt l_ln,  q(10) proneness(l_pop S_somecollege l_mean
> _income S_poor S_manuf div2 div3 div4 div5 div6 div7 div8 div9 elevat_range_msa rug
> gedness_msa heating_dd cooling_dd sprawl i.year) instruments( l_hwy1947 ) optimize(
> grid) grid1(0.1(0.01)2)
i.year            _Iyear_1-3          (_Iyear_1 for year==_1983 omitted)
Grid-search optimization
Number of grid points: 191
 
Generalized Quantile Regression (GQR)
     Observations:                684
 
------------------------------------------------------------------------------
       l_vmt | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
        l_ln |       1.22   .1813801     6.73   0.000     .8645016    1.575498
------------------------------------------------------------------------------
Excluded intstruments: l_hwy1947
Proneness variables: l_pop S_somecollege l_mean_income S_poor S_manuf div2 div3 div4 
> div5 div6 div7 div8 div9 elevat_range_msa ruggedness_msa heating_dd cooling_dd spra
> wl _Iyear_2 _Iyear_3

.                 xi: genqreg l_vmt l_ln,  q(25) proneness(l_pop S_somecollege l_mean
> _income S_poor S_manuf div2 div3 div4 div5 div6 div7 div8 div9 elevat_range_msa rug
> gedness_msa heating_dd cooling_dd sprawl i.year) instruments( l_hwy1947 ) optimize(
> grid) grid1(0.1(0.01)2)         
i.year            _Iyear_1-3          (_Iyear_1 for year==_1983 omitted)
Grid-search optimization
Number of grid points: 191
 
Generalized Quantile Regression (GQR)
     Observations:                684
 
------------------------------------------------------------------------------
       l_vmt | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
        l_ln |       1.25   .1318001     9.48   0.000     .9916765    1.508324
------------------------------------------------------------------------------
Excluded intstruments: l_hwy1947
Proneness variables: l_pop S_somecollege l_mean_income S_poor S_manuf div2 div3 div4 
> div5 div6 div7 div8 div9 elevat_range_msa ruggedness_msa heating_dd cooling_dd spra
> wl _Iyear_2 _Iyear_3

.                 xi: genqreg l_vmt l_ln,  q(50) proneness(l_pop S_somecollege l_mean
> _income S_poor S_manuf div2 div3 div4 div5 div6 div7 div8 div9 elevat_range_msa rug
> gedness_msa heating_dd cooling_dd sprawl i.year) instruments( l_hwy1947 ) optimize(
> grid) grid1(0.1(0.01)2)       
i.year            _Iyear_1-3          (_Iyear_1 for year==_1983 omitted)
Grid-search optimization
Number of grid points: 191
 
Generalized Quantile Regression (GQR)
     Observations:                684
 
------------------------------------------------------------------------------
       l_vmt | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
        l_ln |      1.095    .202817     5.40   0.000     .6974859    1.492514
------------------------------------------------------------------------------
Excluded intstruments: l_hwy1947
Proneness variables: l_pop S_somecollege l_mean_income S_poor S_manuf div2 div3 div4 
> div5 div6 div7 div8 div9 elevat_range_msa ruggedness_msa heating_dd cooling_dd spra
> wl _Iyear_2 _Iyear_3
    Note: Alternative solutions exist. See e(solutions).

.                 xi: genqreg l_vmt l_ln,  q(75) proneness(l_pop S_somecollege l_mean
> _income S_poor S_manuf div2 div3 div4 div5 div6 div7 div8 div9 elevat_range_msa rug
> gedness_msa heating_dd cooling_dd sprawl i.year) instruments( l_hwy1947 ) optimize(
> grid) grid1(0.1(0.01)2)       
i.year            _Iyear_1-3          (_Iyear_1 for year==_1983 omitted)
Grid-search optimization
Number of grid points: 191
 
Generalized Quantile Regression (GQR)
     Observations:                684
 
------------------------------------------------------------------------------
       l_vmt | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
        l_ln |        .55   .3001498     1.83   0.067    -.0382828    1.138283
------------------------------------------------------------------------------
Excluded intstruments: l_hwy1947
Proneness variables: l_pop S_somecollege l_mean_income S_poor S_manuf div2 div3 div4 
> div5 div6 div7 div8 div9 elevat_range_msa ruggedness_msa heating_dd cooling_dd spra
> wl _Iyear_2 _Iyear_3

.                 xi: genqreg l_vmt l_ln,  q(90) proneness(l_pop S_somecollege l_mean
> _income S_poor S_manuf div2 div3 div4 div5 div6 div7 div8 div9 elevat_range_msa rug
> gedness_msa heating_dd cooling_dd sprawl i.year) instruments( l_hwy1947 ) optimize(
> grid) grid1(0.1(0.01)2)      
i.year            _Iyear_1-3          (_Iyear_1 for year==_1983 omitted)
Grid-search optimization
Number of grid points: 191
 
Generalized Quantile Regression (GQR)
     Observations:                684
 
------------------------------------------------------------------------------
       l_vmt | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
        l_ln |       .505   .5688354     0.89   0.375    -.6098968    1.619897
------------------------------------------------------------------------------
Excluded intstruments: l_hwy1947
Proneness variables: l_pop S_somecollege l_mean_income S_poor S_manuf div2 div3 div4 
> div5 div6 div7 div8 div9 elevat_range_msa ruggedness_msa heating_dd cooling_dd spra
> wl _Iyear_2 _Iyear_3
    Note: Alternative solutions exist. See e(solutions).

. 
. 
. 
. * Model 5 *     
. 
.                 xi: ivregress 2sls l_vmt l_pop l_pop80 l_pop70 l_pop60 l_pop50 l_po
> p40 l_pop30 l_pop20 S_somecollege l_mean_income S_poor S_manuf div2 div3 div4 div5 
> div6 div7 div8 div9 elevat_range_msa ruggedness_msa heating_dd cooling_dd sprawl i.
> year (l_ln =  l_hwy1947 ), robust       
i.year            _Iyear_1-3          (_Iyear_1 for year==_1983 omitted)

Instrumental variables 2SLS regression            Number of obs   =        684
                                                  Wald chi2(28)   =   13091.02
                                                  Prob > chi2     =     0.0000
                                                  R-squared       =     0.9442
                                                  Root MSE        =     .32356

----------------------------------------------------------------------------------
                 |               Robust
           l_vmt | Coefficient  std. err.      z    P>|z|     [95% conf. interval]
-----------------+----------------------------------------------------------------
            l_ln |   1.120968   .0926627    12.10   0.000     .9393528    1.302584
           l_pop |   .1944544   .1402892     1.39   0.166    -.0805074    .4694163
         l_pop80 |   .6508502   .3008699     2.16   0.031     .0611561    1.240544
         l_pop70 |  -.0990195   .3787314    -0.26   0.794    -.8413194    .6432805
         l_pop60 |  -.6090298    .298713    -2.04   0.041    -1.194497    -.023563
         l_pop50 |   .0281836   .1856892     0.15   0.879    -.3357606    .3921277
         l_pop40 |  -.0421996   .2008207    -0.21   0.834    -.4358008    .3514017
         l_pop30 |   .1666581     .17037     0.98   0.328     -.167261    .5005771
         l_pop20 |  -.0311454   .0846005    -0.37   0.713    -.1969593    .1346685
   S_somecollege |   .4874998   .3083282     1.58   0.114    -.1168124    1.091812
   l_mean_income |  -.5751417   .2226727    -2.58   0.010    -1.011572   -.1387112
          S_poor |  -.2865847    .369927    -0.77   0.439    -1.011628    .4384589
         S_manuf |   .6388746    .268167     2.38   0.017      .113277    1.164472
            div2 |  -.1627557   .0822418    -1.98   0.048    -.3239466   -.0015649
            div3 |   .0299122     .10374     0.29   0.773    -.1734146    .2332389
            div4 |  -.0837289   .1050508    -0.80   0.425    -.2896247     .122167
            div5 |  -.1593872    .101487    -1.57   0.116    -.3582981    .0395238
            div6 |  -.2533182   .1107671    -2.29   0.022    -.4704176   -.0362187
            div7 |  -.1039064   .1185231    -0.88   0.381    -.3362075    .1283947
            div8 |  -.4588864   .1557926    -2.95   0.003    -.7642342   -.1535385
            div9 |  -.3144056    .147565    -2.13   0.033    -.6036276   -.0251836
elevat_range_msa |  -.0621414   .0330232    -1.88   0.060    -.1268656    .0025828
  ruggedness_msa |   5.438858   2.042281     2.66   0.008     1.436061    9.441656
      heating_dd |  -.0161294   .0029858    -5.40   0.000    -.0219815   -.0102773
      cooling_dd |  -.0299701   .0067518    -4.44   0.000    -.0432033   -.0167369
          sprawl |   .0002619   .0021762     0.12   0.904    -.0040034    .0045272
        _Iyear_2 |   .3610268   .0574462     6.28   0.000     .2484343    .4736193
        _Iyear_3 |   .5685283   .1080237     5.26   0.000     .3568058    .7802508
           _cons |   10.90906   2.033992     5.36   0.000     6.922507    14.89561
----------------------------------------------------------------------------------
Endogenous: l_ln
Exogenous:  l_pop l_pop80 l_pop70 l_pop60 l_pop50 l_pop40 l_pop30 l_pop20
            S_somecollege l_mean_income S_poor S_manuf div2 div3 div4 div5 div6
            div7 div8 div9 elevat_range_msa ruggedness_msa heating_dd cooling_dd
            sprawl _Iyear_2 _Iyear_3 l_hwy1947

.                 
.                 xi: genqreg l_vmt l_ln,  q(10) proneness(l_pop l_pop80 l_pop70 l_po
> p60 l_pop50 l_pop40 l_pop30 l_pop20 S_somecollege l_mean_income S_poor S_manuf div2
>  div3 div4 div5 div6 div7 div8 div9 elevat_range_msa ruggedness_msa heating_dd cool
> ing_dd sprawl i.year) instruments( l_hwy1947 )  optimize(grid) grid1(0.1(0.01)2)
i.year            _Iyear_1-3          (_Iyear_1 for year==_1983 omitted)
Grid-search optimization
Number of grid points: 191
 
Generalized Quantile Regression (GQR)
     Observations:                684
 
------------------------------------------------------------------------------
       l_vmt | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
        l_ln |      1.275   .1474409     8.65   0.000     .9860211    1.563979
------------------------------------------------------------------------------
Excluded intstruments: l_hwy1947
Proneness variables: l_pop l_pop80 l_pop70 l_pop60 l_pop50 l_pop40 l_pop30 l_pop20 S_
> somecollege l_mean_income S_poor S_manuf div2 div3 div4 div5 div6 div7 div8 div9 el
> evat_range_msa ruggedness_msa heating_dd cooling_dd sprawl _Iyear_2 _Iyear_3
    Note: Alternative solutions exist. See e(solutions).

.                 xi: genqreg l_vmt l_ln,  q(25) proneness(l_pop l_pop80 l_pop70 l_po
> p60 l_pop50 l_pop40 l_pop30 l_pop20 S_somecollege l_mean_income S_poor S_manuf div2
>  div3 div4 div5 div6 div7 div8 div9 elevat_range_msa ruggedness_msa heating_dd cool
> ing_dd sprawl i.year) instruments( l_hwy1947 ) optimize(grid) grid1(0.1(0.01)2)
i.year            _Iyear_1-3          (_Iyear_1 for year==_1983 omitted)
Grid-search optimization
Number of grid points: 191
 
Generalized Quantile Regression (GQR)
     Observations:                684
 
------------------------------------------------------------------------------
       l_vmt | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
        l_ln |      1.275   .1235582    10.32   0.000      1.03283     1.51717
------------------------------------------------------------------------------
Excluded intstruments: l_hwy1947
Proneness variables: l_pop l_pop80 l_pop70 l_pop60 l_pop50 l_pop40 l_pop30 l_pop20 S_
> somecollege l_mean_income S_poor S_manuf div2 div3 div4 div5 div6 div7 div8 div9 el
> evat_range_msa ruggedness_msa heating_dd cooling_dd sprawl _Iyear_2 _Iyear_3
    Note: Alternative solutions exist. See e(solutions).

.                 xi: genqreg l_vmt l_ln,  q(50) proneness(l_pop l_pop80 l_pop70 l_po
> p60 l_pop50 l_pop40 l_pop30 l_pop20 S_somecollege l_mean_income S_poor S_manuf div2
>  div3 div4 div5 div6 div7 div8 div9 elevat_range_msa ruggedness_msa heating_dd cool
> ing_dd sprawl i.year) instruments( l_hwy1947 )  optimize(grid) grid1(0.1(0.01)2)  
i.year            _Iyear_1-3          (_Iyear_1 for year==_1983 omitted)
Grid-search optimization
Number of grid points: 191
 
Generalized Quantile Regression (GQR)
     Observations:                684
 
------------------------------------------------------------------------------
       l_vmt | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
        l_ln |        1.2   .4930749     2.43   0.015      .233591    2.166409
------------------------------------------------------------------------------
Excluded intstruments: l_hwy1947
Proneness variables: l_pop l_pop80 l_pop70 l_pop60 l_pop50 l_pop40 l_pop30 l_pop20 S_
> somecollege l_mean_income S_poor S_manuf div2 div3 div4 div5 div6 div7 div8 div9 el
> evat_range_msa ruggedness_msa heating_dd cooling_dd sprawl _Iyear_2 _Iyear_3

.                 xi: genqreg l_vmt l_ln,  q(75) proneness(l_pop l_pop80 l_pop70 l_po
> p60 l_pop50 l_pop40 l_pop30 l_pop20 S_somecollege l_mean_income S_poor S_manuf div2
>  div3 div4 div5 div6 div7 div8 div9 elevat_range_msa ruggedness_msa heating_dd cool
> ing_dd sprawl i.year) instruments( l_hwy1947 ) optimize(grid) grid1(0.1(0.01)2)   
i.year            _Iyear_1-3          (_Iyear_1 for year==_1983 omitted)
Grid-search optimization
Number of grid points: 191
 
Generalized Quantile Regression (GQR)
     Observations:                684
 
------------------------------------------------------------------------------
       l_vmt | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
        l_ln |         .4   .4226944     0.95   0.344    -.4284659    1.228466
------------------------------------------------------------------------------
Excluded intstruments: l_hwy1947
Proneness variables: l_pop l_pop80 l_pop70 l_pop60 l_pop50 l_pop40 l_pop30 l_pop20 S_
> somecollege l_mean_income S_poor S_manuf div2 div3 div4 div5 div6 div7 div8 div9 el
> evat_range_msa ruggedness_msa heating_dd cooling_dd sprawl _Iyear_2 _Iyear_3
    Note: Alternative solutions exist. See e(solutions).

.                 xi: genqreg l_vmt l_ln,  q(90) proneness(l_pop l_pop80 l_pop70 l_po
> p60 l_pop50 l_pop40 l_pop30 l_pop20 S_somecollege l_mean_income S_poor S_manuf div2
>  div3 div4 div5 div6 div7 div8 div9 elevat_range_msa ruggedness_msa heating_dd cool
> ing_dd sprawl i.year) instruments( l_hwy1947 ) optimize(grid) grid1(0.1(0.01)2)    
>     
i.year            _Iyear_1-3          (_Iyear_1 for year==_1983 omitted)
Grid-search optimization
Number of grid points: 191
 
Generalized Quantile Regression (GQR)
     Observations:                684
 
------------------------------------------------------------------------------
       l_vmt | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
        l_ln |       1.47   .0766929    19.17   0.000     1.319685    1.620315
------------------------------------------------------------------------------
Excluded intstruments: l_hwy1947
Proneness variables: l_pop l_pop80 l_pop70 l_pop60 l_pop50 l_pop40 l_pop30 l_pop20 S_
> somecollege l_mean_income S_poor S_manuf div2 div3 div4 div5 div6 div7 div8 div9 el
> evat_range_msa ruggedness_msa heating_dd cooling_dd sprawl _Iyear_2 _Iyear_3

.         
. 
.         
.         
. * Third Panel * 
.                 
.                 local Inst "l_rail1898"

.                 capture drop l_vmt l_ln

.                 gen l_vmt = l_vmt_IH  

.                 gen l_ln  = l_ln_km_IH 

. 
. 
. 
. * Model 1 *     
. 
.                 xi: ivregress 2sls l_vmt i.year (l_ln =  l_rail1898 ), robust      
i.year            _Iyear_1-3          (_Iyear_1 for year==_1983 omitted)

Instrumental variables 2SLS regression            Number of obs   =        684
                                                  Wald chi2(3)    =    1536.48
                                                  Prob > chi2     =     0.0000
                                                  R-squared       =     0.8744
                                                  Root MSE        =      .4853

------------------------------------------------------------------------------
             |               Robust
       l_vmt | Coefficient  std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
        l_ln |   1.313813   .0393074    33.42   0.000     1.236772    1.390854
    _Iyear_2 |   .3990986   .0470344     8.49   0.000     .3069128    .4912843
    _Iyear_3 |   .6653059   .0465901    14.28   0.000      .573991    .7566208
       _cons |   6.293648   .2603771    24.17   0.000     5.783318    6.803978
------------------------------------------------------------------------------
Endogenous: l_ln
Exogenous:  _Iyear_2 _Iyear_3 l_rail1898

. 
.                 xi: genqreg l_vmt l_ln,  q(10) proneness(i.year) instruments( l_rai
> l1898 )  optimize(grid) grid1(0.1(0.01)2)     
i.year            _Iyear_1-3          (_Iyear_1 for year==_1983 omitted)
Grid-search optimization
Number of grid points: 191
 
Generalized Quantile Regression (GQR)
     Observations:                684
 
------------------------------------------------------------------------------
       l_vmt | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
        l_ln |       1.51   .0845864    17.85   0.000     1.344214    1.675786
------------------------------------------------------------------------------
Excluded intstruments: l_rail1898
Proneness variables: _Iyear_2 _Iyear_3

.                 xi: genqreg l_vmt l_ln,  q(25) proneness(i.year) instruments( l_rai
> l1898 )  optimize(grid) grid1(0.1(0.01)2)          
i.year            _Iyear_1-3          (_Iyear_1 for year==_1983 omitted)
Grid-search optimization
Number of grid points: 191
 
Generalized Quantile Regression (GQR)
     Observations:                684
 
------------------------------------------------------------------------------
       l_vmt | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
        l_ln |       1.42   .0825021    17.21   0.000     1.258299    1.581701
------------------------------------------------------------------------------
Excluded intstruments: l_rail1898
Proneness variables: _Iyear_2 _Iyear_3

.                 xi: genqreg l_vmt l_ln,  q(50) proneness(i.year) instruments( l_rai
> l1898 ) optimize(grid) grid1(0.1(0.01)2)             
i.year            _Iyear_1-3          (_Iyear_1 for year==_1983 omitted)
Grid-search optimization
Number of grid points: 191
 
Generalized Quantile Regression (GQR)
     Observations:                684
 
------------------------------------------------------------------------------
       l_vmt | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
        l_ln |        1.3   .0974004    13.35   0.000     1.109099    1.490901
------------------------------------------------------------------------------
Excluded intstruments: l_rail1898
Proneness variables: _Iyear_2 _Iyear_3

.                 xi: genqreg l_vmt l_ln,  q(75) proneness(i.year) instruments( l_rai
> l1898 ) optimize(grid) grid1(0.1(0.01)2)   
i.year            _Iyear_1-3          (_Iyear_1 for year==_1983 omitted)
Grid-search optimization
Number of grid points: 191
 
Generalized Quantile Regression (GQR)
     Observations:                684
 
------------------------------------------------------------------------------
       l_vmt | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
        l_ln |       1.18   .1060288    11.13   0.000     .9721874    1.387813
------------------------------------------------------------------------------
Excluded intstruments: l_rail1898
Proneness variables: _Iyear_2 _Iyear_3

.                 xi: genqreg l_vmt l_ln,  q(90) proneness(i.year) instruments( l_rai
> l1898 )  optimize(grid) grid1(0.1(0.01)2)     
i.year            _Iyear_1-3          (_Iyear_1 for year==_1983 omitted)
Grid-search optimization
Number of grid points: 191
 
Generalized Quantile Regression (GQR)
     Observations:                684
 
------------------------------------------------------------------------------
       l_vmt | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
        l_ln |       1.11   .0499046    22.24   0.000     1.012189    1.207811
------------------------------------------------------------------------------
Excluded intstruments: l_rail1898
Proneness variables: _Iyear_2 _Iyear_3

. 
. 
. 
. * Model 2 *     
. 
.                 xi: ivregress 2sls l_vmt l_pop i.year  (l_ln =  l_rail1898 ), robus
> t                    
i.year            _Iyear_1-3          (_Iyear_1 for year==_1983 omitted)

Instrumental variables 2SLS regression            Number of obs   =        684
                                                  Wald chi2(4)    =    7689.13
                                                  Prob > chi2     =     0.0000
                                                  R-squared       =     0.9360
                                                  Root MSE        =     .34629

------------------------------------------------------------------------------
             |               Robust
       l_vmt | Coefficient  std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
        l_ln |   .8308198   .0956895     8.68   0.000     .6432719    1.018368
       l_pop |   .4691948   .0669826     7.00   0.000     .3379114    .6004783
    _Iyear_2 |   .3964238   .0337989    11.73   0.000     .3301791    .4626684
    _Iyear_3 |   .6273888   .0333302    18.82   0.000     .5620629    .6927148
       _cons |   3.460594   .2702965    12.80   0.000     2.930822    3.990365
------------------------------------------------------------------------------
Endogenous: l_ln
Exogenous:  l_pop _Iyear_2 _Iyear_3 l_rail1898

. 
.                 xi: genqreg l_vmt l_ln,  q(10) proneness(l_pop i.year) instruments(
>  l_rail1898 ) optimize(grid) grid1(0.1(0.01)2)
i.year            _Iyear_1-3          (_Iyear_1 for year==_1983 omitted)
Grid-search optimization
Number of grid points: 191
 
Generalized Quantile Regression (GQR)
     Observations:                684
 
------------------------------------------------------------------------------
       l_vmt | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
        l_ln |       1.22   .1102551    11.07   0.000     1.003904    1.436096
------------------------------------------------------------------------------
Excluded intstruments: l_rail1898
Proneness variables: l_pop _Iyear_2 _Iyear_3

.                 xi: genqreg l_vmt l_ln,  q(25) proneness(l_pop i.year) instruments(
>  l_rail1898 )  optimize(grid) grid1(0.1(0.01)2)   
i.year            _Iyear_1-3          (_Iyear_1 for year==_1983 omitted)
Grid-search optimization
Number of grid points: 191
 
Generalized Quantile Regression (GQR)
     Observations:                684
 
------------------------------------------------------------------------------
       l_vmt | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
        l_ln |       1.22   .1840646     6.63   0.000       .85924     1.58076
------------------------------------------------------------------------------
Excluded intstruments: l_rail1898
Proneness variables: l_pop _Iyear_2 _Iyear_3

.                 xi: genqreg l_vmt l_ln,  q(50) proneness(l_pop i.year) instruments(
>  l_rail1898 ) optimize(grid) grid1(0.1(0.01)2)     
i.year            _Iyear_1-3          (_Iyear_1 for year==_1983 omitted)
Grid-search optimization
Number of grid points: 191
 
Generalized Quantile Regression (GQR)
     Observations:                684
 
------------------------------------------------------------------------------
       l_vmt | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
        l_ln |        .68   .3647759     1.86   0.062    -.0349477    1.394948
------------------------------------------------------------------------------
Excluded intstruments: l_rail1898
Proneness variables: l_pop _Iyear_2 _Iyear_3

.                 xi: genqreg l_vmt l_ln,  q(75) proneness(l_pop i.year) instruments(
>  l_rail1898 ) optimize(grid) grid1(0.1(0.01)2)     
i.year            _Iyear_1-3          (_Iyear_1 for year==_1983 omitted)
Grid-search optimization
Number of grid points: 191
 
Generalized Quantile Regression (GQR)
     Observations:                684
 
------------------------------------------------------------------------------
       l_vmt | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
        l_ln |         .5    .902068     0.55   0.579    -1.268021    2.268021
------------------------------------------------------------------------------
Excluded intstruments: l_rail1898
Proneness variables: l_pop _Iyear_2 _Iyear_3
    Note: Alternative solutions exist. See e(solutions).

.                 xi: genqreg l_vmt l_ln,  q(90) proneness(l_pop i.year) instruments(
>  l_rail1898 )  optimize(grid) grid1(0.1(0.01)2)                              
i.year            _Iyear_1-3          (_Iyear_1 for year==_1983 omitted)
Grid-search optimization
Number of grid points: 191
 
Generalized Quantile Regression (GQR)
     Observations:                684
 
------------------------------------------------------------------------------
       l_vmt | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
        l_ln |        .63   .3128579     2.01   0.044     .0168098     1.24319
------------------------------------------------------------------------------
Excluded intstruments: l_rail1898
Proneness variables: l_pop _Iyear_2 _Iyear_3
    Note: Alternative solutions exist. See e(solutions).

. 
. 
. * Model 3 *     
. 
.                 xi: ivregress 2sls l_vmt l_pop div2 div3 div4 div5 div6 div7 div8 d
> iv9 elevat_range_msa ruggedness_msa heating_dd cooling_dd sprawl i.year  (l_ln =  l
> _rail1898 ), robust                               
i.year            _Iyear_1-3          (_Iyear_1 for year==_1983 omitted)

Instrumental variables 2SLS regression            Number of obs   =        684
                                                  Wald chi2(17)   =   12417.43
                                                  Prob > chi2     =     0.0000
                                                  R-squared       =     0.9453
                                                  Root MSE        =     .32017

----------------------------------------------------------------------------------
                 |               Robust
           l_vmt | Coefficient  std. err.      z    P>|z|     [95% conf. interval]
-----------------+----------------------------------------------------------------
            l_ln |   1.025713   .1136805     9.02   0.000     .8029031    1.248522
           l_pop |   .3047686   .0904469     3.37   0.001      .127496    .4820412
            div2 |  -.1740415   .0639154    -2.72   0.006    -.2993133   -.0487696
            div3 |   .0453234   .0798257     0.57   0.570    -.1111321     .201779
            div4 |  -.0443604   .1027226    -0.43   0.666     -.245693    .1569721
            div5 |  -.0685146   .0987507    -0.69   0.488    -.2620624    .1250332
            div6 |  -.1725231   .1089175    -1.58   0.113    -.3859974    .0409512
            div7 |  -.1331172   .1172593    -1.14   0.256    -.3629413    .0967069
            div8 |  -.3783429    .157195    -2.41   0.016    -.6864396   -.0702463
            div9 |  -.3288215   .1319736    -2.49   0.013    -.5874849    -.070158
elevat_range_msa |   -.026167   .0348913    -0.75   0.453    -.0945528    .0422187
  ruggedness_msa |   6.668191   2.063906     3.23   0.001     2.623009    10.71337
      heating_dd |  -.0147949    .002821    -5.24   0.000    -.0203239   -.0092659
      cooling_dd |  -.0223321   .0055851    -4.00   0.000    -.0332788   -.0113854
          sprawl |   .0013906   .0021029     0.66   0.508    -.0027311    .0055122
        _Iyear_2 |    .394246   .0307025    12.84   0.000     .3340701    .4544218
        _Iyear_3 |   .6363618   .0307579    20.69   0.000     .5760773    .6966462
           _cons |   5.296225   .6820359     7.77   0.000     3.959459    6.632991
----------------------------------------------------------------------------------
Endogenous: l_ln
Exogenous:  l_pop div2 div3 div4 div5 div6 div7 div8 div9 elevat_range_msa
            ruggedness_msa heating_dd cooling_dd sprawl _Iyear_2 _Iyear_3
            l_rail1898

.                 
.                 xi: genqreg l_vmt l_ln,  q(10) proneness(l_pop div2 div3 div4 div5 
> div6 div7 div8 div9 elevat_range_msa ruggedness_msa heating_dd cooling_dd sprawl i.
> year) instruments( l_rail1898 ) optimize(grid) grid1(0.1(0.01)2)                  
i.year            _Iyear_1-3          (_Iyear_1 for year==_1983 omitted)
Grid-search optimization
Number of grid points: 191
 
Generalized Quantile Regression (GQR)
     Observations:                684
 
------------------------------------------------------------------------------
       l_vmt | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
        l_ln |       1.39   .1340724    10.37   0.000     1.127223    1.652777
------------------------------------------------------------------------------
Excluded intstruments: l_rail1898
Proneness variables: l_pop div2 div3 div4 div5 div6 div7 div8 div9 elevat_range_msa r
> uggedness_msa heating_dd cooling_dd sprawl _Iyear_2 _Iyear_3

.                 xi: genqreg l_vmt l_ln,  q(25) proneness(l_pop div2 div3 div4 div5 
> div6 div7 div8 div9 elevat_range_msa ruggedness_msa heating_dd cooling_dd sprawl i.
> year) instruments( l_rail1898 ) optimize(grid) grid1(0.1(0.01)2)                   
>     
i.year            _Iyear_1-3          (_Iyear_1 for year==_1983 omitted)
Grid-search optimization
Number of grid points: 191
 
Generalized Quantile Regression (GQR)
     Observations:                684
 
------------------------------------------------------------------------------
       l_vmt | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
        l_ln |      1.335   .0940204    14.20   0.000     1.150723    1.519277
------------------------------------------------------------------------------
Excluded intstruments: l_rail1898
Proneness variables: l_pop div2 div3 div4 div5 div6 div7 div8 div9 elevat_range_msa r
> uggedness_msa heating_dd cooling_dd sprawl _Iyear_2 _Iyear_3
    Note: Alternative solutions exist. See e(solutions).

.                 xi: genqreg l_vmt l_ln,  q(50) proneness(l_pop div2 div3 div4 div5 
> div6 div7 div8 div9 elevat_range_msa ruggedness_msa heating_dd cooling_dd sprawl i.
> year) instruments( l_rail1898 ) optimize(grid) grid1(0.1(0.01)2)                   
>   
i.year            _Iyear_1-3          (_Iyear_1 for year==_1983 omitted)
Grid-search optimization
Number of grid points: 191
 
Generalized Quantile Regression (GQR)
     Observations:                684
 
------------------------------------------------------------------------------
       l_vmt | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
        l_ln |       1.08   .2180954     4.95   0.000     .6525409    1.507459
------------------------------------------------------------------------------
Excluded intstruments: l_rail1898
Proneness variables: l_pop div2 div3 div4 div5 div6 div7 div8 div9 elevat_range_msa r
> uggedness_msa heating_dd cooling_dd sprawl _Iyear_2 _Iyear_3

.                 xi: genqreg l_vmt l_ln,  q(75) proneness(l_pop div2 div3 div4 div5 
> div6 div7 div8 div9 elevat_range_msa ruggedness_msa heating_dd cooling_dd sprawl i.
> year) instruments( l_rail1898 )  optimize(grid) grid1(0.1(0.05)2)                 
i.year            _Iyear_1-3          (_Iyear_1 for year==_1983 omitted)
Grid-search optimization
Number of grid points: 39
 
Generalized Quantile Regression (GQR)
     Observations:                684
 
------------------------------------------------------------------------------
       l_vmt | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
        l_ln |        .85   .3887407     2.19   0.029     .0880823    1.611918
------------------------------------------------------------------------------
Excluded intstruments: l_rail1898
Proneness variables: l_pop div2 div3 div4 div5 div6 div7 div8 div9 elevat_range_msa r
> uggedness_msa heating_dd cooling_dd sprawl _Iyear_2 _Iyear_3

.                 xi: genqreg l_vmt l_ln,  q(90) proneness(l_pop div2 div3 div4 div5 
> div6 div7 div8 div9 elevat_range_msa ruggedness_msa heating_dd cooling_dd sprawl i.
> year) instruments( l_rail1898 )  optimize(grid) grid1(0.1(0.01)2)                  
>     
i.year            _Iyear_1-3          (_Iyear_1 for year==_1983 omitted)
Grid-search optimization
Number of grid points: 191
 
Generalized Quantile Regression (GQR)
     Observations:                684
 
------------------------------------------------------------------------------
       l_vmt | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
        l_ln |       .575   .6932457     0.83   0.407    -.7837366    1.933737
------------------------------------------------------------------------------
Excluded intstruments: l_rail1898
Proneness variables: l_pop div2 div3 div4 div5 div6 div7 div8 div9 elevat_range_msa r
> uggedness_msa heating_dd cooling_dd sprawl _Iyear_2 _Iyear_3
    Note: Alternative solutions exist. See e(solutions).

. 
. 
. * Model 4 *     
. 
.                 xi: ivregress 2sls l_vmt l_pop S_somecollege l_mean_income S_poor S
> _manuf div2 div3 div4 div5 div6 div7 div8 div9 elevat_range_msa ruggedness_msa heat
> ing_dd cooling_dd sprawl i.year (l_ln =  l_rail1898 ), robust                      
>                
i.year            _Iyear_1-3          (_Iyear_1 for year==_1983 omitted)

Instrumental variables 2SLS regression            Number of obs   =        684
                                                  Wald chi2(21)   =   13484.55
                                                  Prob > chi2     =     0.0000
                                                  R-squared       =     0.9492
                                                  Root MSE        =     .30858

----------------------------------------------------------------------------------
                 |               Robust
           l_vmt | Coefficient  std. err.      z    P>|z|     [95% conf. interval]
-----------------+----------------------------------------------------------------
            l_ln |   .9996302    .110914     9.01   0.000     .7822426    1.217018
           l_pop |   .3508872   .0877359     4.00   0.000      .178928    .5228464
   S_somecollege |   1.049819   .2226508     4.72   0.000     .6134312    1.486206
   l_mean_income |  -.7404591   .2130044    -3.48   0.001     -1.15794   -.3229781
          S_poor |   -.518726   .3518197    -1.47   0.140     -1.20828    .1708279
         S_manuf |    .504818   .3193398     1.58   0.114    -.1210766    1.130713
            div2 |  -.1212761   .0734843    -1.65   0.099    -.2653026    .0227504
            div3 |   .1125746   .0945313     1.19   0.234    -.0727034    .2978525
            div4 |  -.0352704   .1032982    -0.34   0.733    -.2377311    .1671903
            div5 |  -.0809932   .0939684    -0.86   0.389    -.2651679    .1031814
            div6 |  -.1730638   .1120387    -1.54   0.122    -.3926556     .046528
            div7 |  -.0678017   .1187337    -0.57   0.568    -.3005155    .1649122
            div8 |  -.2974042   .1364372    -2.18   0.029    -.5648163   -.0299921
            div9 |  -.2331249     .11748    -1.98   0.047    -.4633815   -.0028684
elevat_range_msa |   -.051476   .0314581    -1.64   0.102    -.1131328    .0101808
  ruggedness_msa |   6.751279   1.936501     3.49   0.000     2.955807    10.54675
      heating_dd |  -.0136213   .0025978    -5.24   0.000    -.0187129   -.0085296
      cooling_dd |  -.0185056   .0056134    -3.30   0.001    -.0295076   -.0075036
          sprawl |   .0023014   .0020992     1.10   0.273     -.001813    .0064158
        _Iyear_2 |   .2745891   .0501138     5.48   0.000     .1763678    .3728104
        _Iyear_3 |   .3892854   .0914941     4.25   0.000     .2099603    .5686105
           _cons |   11.73015   1.927587     6.09   0.000     7.952147    15.50815
----------------------------------------------------------------------------------
Endogenous: l_ln
Exogenous:  l_pop S_somecollege l_mean_income S_poor S_manuf div2 div3 div4 div5
            div6 div7 div8 div9 elevat_range_msa ruggedness_msa heating_dd
            cooling_dd sprawl _Iyear_2 _Iyear_3 l_rail1898

.                 
.                 xi: genqreg l_vmt l_ln,  q(10) proneness(l_pop S_somecollege l_mean
> _income S_poor S_manuf div2 div3 div4 div5 div6 div7 div8 div9 elevat_range_msa rug
> gedness_msa heating_dd cooling_dd sprawl i.year) instruments( l_rail1898 ) optimize
> (grid) grid1(0.1(0.01)2)               
i.year            _Iyear_1-3          (_Iyear_1 for year==_1983 omitted)
Grid-search optimization
Number of grid points: 191
 
Generalized Quantile Regression (GQR)
     Observations:                684
 
------------------------------------------------------------------------------
       l_vmt | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
        l_ln |       1.36   .1464911     9.28   0.000     1.072883    1.647117
------------------------------------------------------------------------------
Excluded intstruments: l_rail1898
Proneness variables: l_pop S_somecollege l_mean_income S_poor S_manuf div2 div3 div4 
> div5 div6 div7 div8 div9 elevat_range_msa ruggedness_msa heating_dd cooling_dd spra
> wl _Iyear_2 _Iyear_3

.                 xi: genqreg l_vmt l_ln,  q(25) proneness(l_pop S_somecollege l_mean
> _income S_poor S_manuf div2 div3 div4 div5 div6 div7 div8 div9 elevat_range_msa rug
> gedness_msa heating_dd cooling_dd sprawl i.year) instruments( l_rail1898 ) optimize
> (grid) grid1(0.1(0.01)2)         
i.year            _Iyear_1-3          (_Iyear_1 for year==_1983 omitted)
Grid-search optimization
Number of grid points: 191
 
Generalized Quantile Regression (GQR)
     Observations:                684
 
------------------------------------------------------------------------------
       l_vmt | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
        l_ln |       1.32   .0951586    13.87   0.000     1.133493    1.506507
------------------------------------------------------------------------------
Excluded intstruments: l_rail1898
Proneness variables: l_pop S_somecollege l_mean_income S_poor S_manuf div2 div3 div4 
> div5 div6 div7 div8 div9 elevat_range_msa ruggedness_msa heating_dd cooling_dd spra
> wl _Iyear_2 _Iyear_3

.                 xi: genqreg l_vmt l_ln,  q(50) proneness(l_pop S_somecollege l_mean
> _income S_poor S_manuf div2 div3 div4 div5 div6 div7 div8 div9 elevat_range_msa rug
> gedness_msa heating_dd cooling_dd sprawl i.year) instruments( l_rail1898 ) optimize
> (grid) grid1(0.1(0.01)2)       
i.year            _Iyear_1-3          (_Iyear_1 for year==_1983 omitted)
Grid-search optimization
Number of grid points: 191
 
Generalized Quantile Regression (GQR)
     Observations:                684
 
------------------------------------------------------------------------------
       l_vmt | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
        l_ln |       1.08   .2581278     4.18   0.000     .5740789    1.585921
------------------------------------------------------------------------------
Excluded intstruments: l_rail1898
Proneness variables: l_pop S_somecollege l_mean_income S_poor S_manuf div2 div3 div4 
> div5 div6 div7 div8 div9 elevat_range_msa ruggedness_msa heating_dd cooling_dd spra
> wl _Iyear_2 _Iyear_3

.                 xi: genqreg l_vmt l_ln,  q(75) proneness(l_pop S_somecollege l_mean
> _income S_poor S_manuf div2 div3 div4 div5 div6 div7 div8 div9 elevat_range_msa rug
> gedness_msa heating_dd cooling_dd sprawl i.year) instruments( l_rail1898 ) optimize
> (grid) grid1(0.1(0.01)2)       
i.year            _Iyear_1-3          (_Iyear_1 for year==_1983 omitted)
Grid-search optimization
Number of grid points: 191
 
Generalized Quantile Regression (GQR)
     Observations:                684
 
------------------------------------------------------------------------------
       l_vmt | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
        l_ln |        .59   .4171357     1.41   0.157     -.227571    1.407571
------------------------------------------------------------------------------
Excluded intstruments: l_rail1898
Proneness variables: l_pop S_somecollege l_mean_income S_poor S_manuf div2 div3 div4 
> div5 div6 div7 div8 div9 elevat_range_msa ruggedness_msa heating_dd cooling_dd spra
> wl _Iyear_2 _Iyear_3

.                 xi: genqreg l_vmt l_ln,  q(90) proneness(l_pop S_somecollege l_mean
> _income S_poor S_manuf div2 div3 div4 div5 div6 div7 div8 div9 elevat_range_msa rug
> gedness_msa heating_dd cooling_dd sprawl i.year) instruments( l_rail1898 ) optimize
> (grid) grid1(0.1(0.01)2)        
i.year            _Iyear_1-3          (_Iyear_1 for year==_1983 omitted)
Grid-search optimization
Number of grid points: 191
 
Generalized Quantile Regression (GQR)
     Observations:                684
 
------------------------------------------------------------------------------
       l_vmt | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
        l_ln |       .575   .9539009     0.60   0.547    -1.294611    2.444611
------------------------------------------------------------------------------
Excluded intstruments: l_rail1898
Proneness variables: l_pop S_somecollege l_mean_income S_poor S_manuf div2 div3 div4 
> div5 div6 div7 div8 div9 elevat_range_msa ruggedness_msa heating_dd cooling_dd spra
> wl _Iyear_2 _Iyear_3
    Note: Alternative solutions exist. See e(solutions).

. 
. 
. 
. * Model 5 *     
. 
.                 xi: ivregress 2sls l_vmt l_pop l_pop80 l_pop70 l_pop60 l_pop50 l_po
> p40 l_pop30 l_pop20 S_somecollege l_mean_income S_poor S_manuf div2 div3 div4 div5 
> div6 div7 div8 div9 elevat_range_msa ruggedness_msa heating_dd cooling_dd sprawl i.
> year (l_ln =  l_rail1898 ), robust      
i.year            _Iyear_1-3          (_Iyear_1 for year==_1983 omitted)

Instrumental variables 2SLS regression            Number of obs   =        684
                                                  Wald chi2(28)   =   14310.83
                                                  Prob > chi2     =     0.0000
                                                  R-squared       =     0.9507
                                                  Root MSE        =     .30399

----------------------------------------------------------------------------------
                 |               Robust
           l_vmt | Coefficient  std. err.      z    P>|z|     [95% conf. interval]
-----------------+----------------------------------------------------------------
            l_ln |   1.022088    .137705     7.42   0.000     .7521909    1.291985
           l_pop |   .2417284   .1371422     1.76   0.078    -.0270654    .5105223
         l_pop80 |   .6795412   .2874029     2.36   0.018     .1162419     1.24284
         l_pop70 |  -.2084247   .3650167    -0.57   0.568    -.9238443    .5069949
         l_pop60 |  -.4785935   .3091629    -1.55   0.122    -1.084542    .1273547
         l_pop50 |  -.0469247   .1911333    -0.25   0.806    -.4215391    .3276898
         l_pop40 |   .0158032   .2037481     0.08   0.938    -.3835358    .4151423
         l_pop30 |    .134418   .1695554     0.79   0.428    -.1979044    .4667405
         l_pop20 |  -.0015502    .084959    -0.02   0.985    -.1680668    .1649664
   S_somecollege |   .6136215   .3180797     1.93   0.054    -.0098033    1.237046
   l_mean_income |  -.5981991   .2110945    -2.83   0.005    -1.011937   -.1844615
          S_poor |  -.3296387   .3546488    -0.93   0.353    -1.024738    .3654601
         S_manuf |    .445196   .3243965     1.37   0.170    -.1906094    1.081001
            div2 |  -.1355561   .0778408    -1.74   0.082    -.2881212     .017009
            div3 |    .087299   .1070936     0.82   0.415    -.1226006    .2971987
            div4 |   -.031612   .1060992    -0.30   0.766    -.2395627    .1763386
            div5 |  -.1168969   .1016191    -1.15   0.250    -.3160667    .0822729
            div6 |  -.1941663   .1198491    -1.62   0.105    -.4290663    .0407337
            div7 |  -.0608424   .1184282    -0.51   0.607    -.2929574    .1712727
            div8 |  -.3632085   .1713468    -2.12   0.034     -.699042    -.027375
            div9 |  -.2587489   .1452377    -1.78   0.075    -.5434096    .0259118
elevat_range_msa |  -.0571508   .0308501    -1.85   0.064     -.117616    .0033143
  ruggedness_msa |   5.030591   1.922496     2.62   0.009     1.262568    8.798614
      heating_dd |  -.0156554    .002769    -5.65   0.000    -.0210826   -.0102282
      cooling_dd |  -.0283085   .0065843    -4.30   0.000    -.0412136   -.0154035
          sprawl |   .0013379   .0022657     0.59   0.555    -.0031028    .0057785
        _Iyear_2 |   .3382951   .0590051     5.73   0.000     .2226472     .453943
        _Iyear_3 |   .5206866   .1135378     4.59   0.000     .2981566    .7432165
           _cons |    10.7029   1.910158     5.60   0.000     6.959062    14.44674
----------------------------------------------------------------------------------
Endogenous: l_ln
Exogenous:  l_pop l_pop80 l_pop70 l_pop60 l_pop50 l_pop40 l_pop30 l_pop20
            S_somecollege l_mean_income S_poor S_manuf div2 div3 div4 div5 div6
            div7 div8 div9 elevat_range_msa ruggedness_msa heating_dd cooling_dd
            sprawl _Iyear_2 _Iyear_3 l_rail1898

.                 
.                 xi: genqreg l_vmt l_ln,  q(10) proneness(l_pop l_pop80 l_pop70 l_po
> p60 l_pop50 l_pop40 l_pop30 l_pop20 S_somecollege l_mean_income S_poor S_manuf div2
>  div3 div4 div5 div6 div7 div8 div9 elevat_range_msa ruggedness_msa heating_dd cool
> ing_dd sprawl i.year) instruments( l_rail1898 )  optimize(grid) grid1(0.1(0.01)2)  
>             
i.year            _Iyear_1-3          (_Iyear_1 for year==_1983 omitted)
Grid-search optimization
Number of grid points: 191
 
Generalized Quantile Regression (GQR)
     Observations:                684
 
------------------------------------------------------------------------------
       l_vmt | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
        l_ln |      1.335   .1479483     9.02   0.000     1.045027    1.624973
------------------------------------------------------------------------------
Excluded intstruments: l_rail1898
Proneness variables: l_pop l_pop80 l_pop70 l_pop60 l_pop50 l_pop40 l_pop30 l_pop20 S_
> somecollege l_mean_income S_poor S_manuf div2 div3 div4 div5 div6 div7 div8 div9 el
> evat_range_msa ruggedness_msa heating_dd cooling_dd sprawl _Iyear_2 _Iyear_3
    Note: Alternative solutions exist. See e(solutions).

.                 xi: genqreg l_vmt l_ln,  q(25) proneness(l_pop l_pop80 l_pop70 l_po
> p60 l_pop50 l_pop40 l_pop30 l_pop20 S_somecollege l_mean_income S_poor S_manuf div2
>  div3 div4 div5 div6 div7 div8 div9 elevat_range_msa ruggedness_msa heating_dd cool
> ing_dd sprawl i.year) instruments( l_rail1898 )  optimize(grid) grid1(0.1(0.01)2)  
>   
i.year            _Iyear_1-3          (_Iyear_1 for year==_1983 omitted)
Grid-search optimization
Number of grid points: 191
 
Generalized Quantile Regression (GQR)
     Observations:                684
 
------------------------------------------------------------------------------
       l_vmt | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
        l_ln |      1.205   .1457963     8.26   0.000     .9192444    1.490756
------------------------------------------------------------------------------
Excluded intstruments: l_rail1898
Proneness variables: l_pop l_pop80 l_pop70 l_pop60 l_pop50 l_pop40 l_pop30 l_pop20 S_
> somecollege l_mean_income S_poor S_manuf div2 div3 div4 div5 div6 div7 div8 div9 el
> evat_range_msa ruggedness_msa heating_dd cooling_dd sprawl _Iyear_2 _Iyear_3
    Note: Alternative solutions exist. See e(solutions).

.                 xi: genqreg l_vmt l_ln,  q(50) proneness(l_pop l_pop80 l_pop70 l_po
> p60 l_pop50 l_pop40 l_pop30 l_pop20 S_somecollege l_mean_income S_poor S_manuf div2
>  div3 div4 div5 div6 div7 div8 div9 elevat_range_msa ruggedness_msa heating_dd cool
> ing_dd sprawl i.year) instruments( l_rail1898 )  optimize(grid) grid1(0.1(0.01)2)  
i.year            _Iyear_1-3          (_Iyear_1 for year==_1983 omitted)
Grid-search optimization
Number of grid points: 191
 
Generalized Quantile Regression (GQR)
     Observations:                684
 
------------------------------------------------------------------------------
       l_vmt | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
        l_ln |       1.14   .2184107     5.22   0.000     .7119229    1.568077
------------------------------------------------------------------------------
Excluded intstruments: l_rail1898
Proneness variables: l_pop l_pop80 l_pop70 l_pop60 l_pop50 l_pop40 l_pop30 l_pop20 S_
> somecollege l_mean_income S_poor S_manuf div2 div3 div4 div5 div6 div7 div8 div9 el
> evat_range_msa ruggedness_msa heating_dd cooling_dd sprawl _Iyear_2 _Iyear_3

.                 xi: genqreg l_vmt l_ln,  q(75) proneness(l_pop l_pop80 l_pop70 l_po
> p60 l_pop50 l_pop40 l_pop30 l_pop20 S_somecollege l_mean_income S_poor S_manuf div2
>  div3 div4 div5 div6 div7 div8 div9 elevat_range_msa ruggedness_msa heating_dd cool
> ing_dd sprawl i.year) instruments( l_rail1898 ) optimize(grid) grid1(0.1(0.01)2)   
i.year            _Iyear_1-3          (_Iyear_1 for year==_1983 omitted)
Grid-search optimization
Number of grid points: 191
 
Generalized Quantile Regression (GQR)
     Observations:                684
 
------------------------------------------------------------------------------
       l_vmt | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
        l_ln |       .255   1.046272     0.24   0.807    -1.795655    2.305655
------------------------------------------------------------------------------
Excluded intstruments: l_rail1898
Proneness variables: l_pop l_pop80 l_pop70 l_pop60 l_pop50 l_pop40 l_pop30 l_pop20 S_
> somecollege l_mean_income S_poor S_manuf div2 div3 div4 div5 div6 div7 div8 div9 el
> evat_range_msa ruggedness_msa heating_dd cooling_dd sprawl _Iyear_2 _Iyear_3
    Note: Alternative solutions exist. See e(solutions).

.                 xi: genqreg l_vmt l_ln,  q(90) proneness(l_pop l_pop80 l_pop70 l_po
> p60 l_pop50 l_pop40 l_pop30 l_pop20 S_somecollege l_mean_income S_poor S_manuf div2
>  div3 div4 div5 div6 div7 div8 div9 elevat_range_msa ruggedness_msa heating_dd cool
> ing_dd sprawl i.year) instruments( l_rail1898 ) optimize(grid) grid1(0.1(0.01)2)   
>     
i.year            _Iyear_1-3          (_Iyear_1 for year==_1983 omitted)
Grid-search optimization
Number of grid points: 191
 
Generalized Quantile Regression (GQR)
     Observations:                684
 
------------------------------------------------------------------------------
       l_vmt | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
        l_ln |       1.33   .0757857    17.55   0.000     1.181463    1.478537
------------------------------------------------------------------------------
Excluded intstruments: l_rail1898
Proneness variables: l_pop l_pop80 l_pop70 l_pop60 l_pop50 l_pop40 l_pop30 l_pop20 S_
> somecollege l_mean_income S_poor S_manuf div2 div3 div4 div5 div6 div7 div8 div9 el
> evat_range_msa ruggedness_msa heating_dd cooling_dd sprawl _Iyear_2 _Iyear_3

.         
. 
.         
.         
. * Fourth Panel *        
.                 
.                 local Inst "l_pix1835"

.                 capture drop l_vmt l_ln

.                 gen l_vmt = l_vmt_IH  

.                 gen l_ln  = l_ln_km_IH 

. 
. 
. 
. 
. * Model 1 *     
. 
.                 xi: ivregress 2sls l_vmt i.year (l_ln =  l_pix1835 ), robust    
i.year            _Iyear_1-3          (_Iyear_1 for year==_1983 omitted)

Instrumental variables 2SLS regression            Number of obs   =        684
                                                  Wald chi2(3)    =    1034.56
                                                  Prob > chi2     =     0.0000
                                                  R-squared       =     0.8770
                                                  Root MSE        =     .48009

------------------------------------------------------------------------------
             |               Robust
       l_vmt | Coefficient  std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
        l_ln |   1.249547   .0472133    26.47   0.000      1.15701    1.342083
    _Iyear_2 |   .4047518   .0464308     8.72   0.000     .3137492    .4957544
    _Iyear_3 |   .6739672   .0457711    14.72   0.000     .5842575    .7636769
       _cons |   6.709531   .3121219    21.50   0.000     6.097784    7.321279
------------------------------------------------------------------------------
Endogenous: l_ln
Exogenous:  _Iyear_2 _Iyear_3 l_pix1835

. 
.                 xi: genqreg l_vmt l_ln,  q(10) proneness(i.year) instruments( l_pix
> 1835 ) optimize(grid) grid1(0.1(0.01)2)      
i.year            _Iyear_1-3          (_Iyear_1 for year==_1983 omitted)
Grid-search optimization
Number of grid points: 191
 
Generalized Quantile Regression (GQR)
     Observations:                684
 
------------------------------------------------------------------------------
       l_vmt | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
        l_ln |      1.435   .2396544     5.99   0.000     .9652861    1.904714
------------------------------------------------------------------------------
Excluded intstruments: l_pix1835
Proneness variables: _Iyear_2 _Iyear_3
    Note: Alternative solutions exist. See e(solutions).

.                 xi: genqreg l_vmt l_ln,  q(25) proneness(i.year) instruments( l_pix
> 1835 )  optimize(grid) grid1(0.1(0.01)2)          
i.year            _Iyear_1-3          (_Iyear_1 for year==_1983 omitted)
Grid-search optimization
Number of grid points: 191
 
Generalized Quantile Regression (GQR)
     Observations:                684
 
------------------------------------------------------------------------------
       l_vmt | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
        l_ln |      1.395   .0770357    18.11   0.000     1.244013    1.545987
------------------------------------------------------------------------------
Excluded intstruments: l_pix1835
Proneness variables: _Iyear_2 _Iyear_3
    Note: Alternative solutions exist. See e(solutions).

.                 xi: genqreg l_vmt l_ln,  q(50) proneness(i.year) instruments( l_pix
> 1835 )  optimize(grid) grid1(0.1(0.01)2)            
i.year            _Iyear_1-3          (_Iyear_1 for year==_1983 omitted)
Grid-search optimization
Number of grid points: 191
 
Generalized Quantile Regression (GQR)
     Observations:                684
 
------------------------------------------------------------------------------
       l_vmt | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
        l_ln |       1.21    .210312     5.75   0.000     .7977961    1.622204
------------------------------------------------------------------------------
Excluded intstruments: l_pix1835
Proneness variables: _Iyear_2 _Iyear_3

.                 xi: genqreg l_vmt l_ln,  q(75) proneness(i.year) instruments( l_pix
> 1835 ) optimize(grid) grid1(0.1(0.01)2)   
i.year            _Iyear_1-3          (_Iyear_1 for year==_1983 omitted)
Grid-search optimization
Number of grid points: 191
 
Generalized Quantile Regression (GQR)
     Observations:                684
 
------------------------------------------------------------------------------
       l_vmt | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
        l_ln |       1.16    .102182    11.35   0.000      .959727    1.360273
------------------------------------------------------------------------------
Excluded intstruments: l_pix1835
Proneness variables: _Iyear_2 _Iyear_3

.                 xi: genqreg l_vmt l_ln,  q(90) proneness(i.year) instruments( l_pix
> 1835 ) optimize(grid) grid1(0.1(0.01)2)      
i.year            _Iyear_1-3          (_Iyear_1 for year==_1983 omitted)
Grid-search optimization
Number of grid points: 191
 
Generalized Quantile Regression (GQR)
     Observations:                684
 
------------------------------------------------------------------------------
       l_vmt | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
        l_ln |       1.11   .0469425    23.65   0.000     1.017994    1.202006
------------------------------------------------------------------------------
Excluded intstruments: l_pix1835
Proneness variables: _Iyear_2 _Iyear_3

. 
. 
. 
. * Model 2 *     
. 
.                 xi: ivregress 2sls l_vmt l_pop i.year  (l_ln =  l_pix1835 ), robust
>                      
i.year            _Iyear_1-3          (_Iyear_1 for year==_1983 omitted)

Instrumental variables 2SLS regression            Number of obs   =        684
                                                  Wald chi2(4)    =    7811.79
                                                  Prob > chi2     =     0.0000
                                                  R-squared       =     0.9289
                                                  Root MSE        =     .36496

------------------------------------------------------------------------------
             |               Robust
       l_vmt | Coefficient  std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
        l_ln |   .6336354   .1105657     5.73   0.000     .4169306    .8503402
       l_pop |    .605914   .0754651     8.03   0.000     .4580051     .753823
    _Iyear_2 |   .4006095    .036133    11.09   0.000     .3297901    .4714289
    _Iyear_3 |   .6239473   .0362381    17.22   0.000     .5529219    .6949727
       _cons |   3.000334   .2741643    10.94   0.000     2.462982    3.537687
------------------------------------------------------------------------------
Endogenous: l_ln
Exogenous:  l_pop _Iyear_2 _Iyear_3 l_pix1835

. 
.                 xi: genqreg l_vmt l_ln,  q(10) proneness(l_pop i.year) instruments(
>  l_pix1835 ) optimize(grid) grid1(0.1(0.01)2)
i.year            _Iyear_1-3          (_Iyear_1 for year==_1983 omitted)
Grid-search optimization
Number of grid points: 191
 
Generalized Quantile Regression (GQR)
     Observations:                684
 
------------------------------------------------------------------------------
       l_vmt | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
        l_ln |        .73   .1733239     4.21   0.000     .3902914    1.069709
------------------------------------------------------------------------------
Excluded intstruments: l_pix1835
Proneness variables: l_pop _Iyear_2 _Iyear_3

.                 xi: genqreg l_vmt l_ln,  q(25) proneness(l_pop i.year) instruments(
>  l_pix1835 ) optimize(grid) grid1(0.1(0.01)2)    
i.year            _Iyear_1-3          (_Iyear_1 for year==_1983 omitted)
Grid-search optimization
Number of grid points: 191
 
Generalized Quantile Regression (GQR)
     Observations:                684
 
------------------------------------------------------------------------------
       l_vmt | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
        l_ln |       .555   .2705434     2.05   0.040     .0247447    1.085255
------------------------------------------------------------------------------
Excluded intstruments: l_pix1835
Proneness variables: l_pop _Iyear_2 _Iyear_3
    Note: Alternative solutions exist. See e(solutions).

.                 xi: genqreg l_vmt l_ln,  q(50) proneness(l_pop i.year) instruments(
>  l_pix1835 ) optimize(grid) grid1(0.1(0.01)2)     
i.year            _Iyear_1-3          (_Iyear_1 for year==_1983 omitted)
Grid-search optimization
Number of grid points: 191
 
Generalized Quantile Regression (GQR)
     Observations:                684
 
------------------------------------------------------------------------------
       l_vmt | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
        l_ln |        .47   .4142434     1.13   0.257    -.3419022    1.281902
------------------------------------------------------------------------------
Excluded intstruments: l_pix1835
Proneness variables: l_pop _Iyear_2 _Iyear_3

.                 xi: genqreg l_vmt l_ln,  q(75) proneness(l_pop i.year) instruments(
>  l_pix1835 ) optimize(grid) grid1(0.1(0.01)2)     
i.year            _Iyear_1-3          (_Iyear_1 for year==_1983 omitted)
Grid-search optimization
Number of grid points: 191
 
Generalized Quantile Regression (GQR)
     Observations:                684
 
------------------------------------------------------------------------------
       l_vmt | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
        l_ln |        .81    .537652     1.51   0.132    -.2437786    1.863779
------------------------------------------------------------------------------
Excluded intstruments: l_pix1835
Proneness variables: l_pop _Iyear_2 _Iyear_3

.                 xi: genqreg l_vmt l_ln,  q(90) proneness(l_pop i.year) instruments(
>  l_pix1835 )  optimize(grid) grid1(0.1(0.01)2)       
i.year            _Iyear_1-3          (_Iyear_1 for year==_1983 omitted)
Grid-search optimization
Number of grid points: 191
 
Generalized Quantile Regression (GQR)
     Observations:                684
 
------------------------------------------------------------------------------
       l_vmt | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
        l_ln |        .32   .6511017     0.49   0.623    -.9561358    1.596136
------------------------------------------------------------------------------
Excluded intstruments: l_pix1835
Proneness variables: l_pop _Iyear_2 _Iyear_3

. 
. 
. * Model 3 *     
. 
.                 xi: ivregress 2sls l_vmt l_pop div2 div3 div4 div5 div6 div7 div8 d
> iv9 elevat_range_msa ruggedness_msa heating_dd cooling_dd sprawl i.year  (l_ln =  l
> _pix1835 ), robust                                
i.year            _Iyear_1-3          (_Iyear_1 for year==_1983 omitted)

Instrumental variables 2SLS regression            Number of obs   =        684
                                                  Wald chi2(17)   =   14897.34
                                                  Prob > chi2     =     0.0000
                                                  R-squared       =     0.9479
                                                  Root MSE        =     .31254

----------------------------------------------------------------------------------
                 |               Robust
           l_vmt | Coefficient  std. err.      z    P>|z|     [95% conf. interval]
-----------------+----------------------------------------------------------------
            l_ln |   .7468345   .1171014     6.38   0.000     .5173201     .976349
           l_pop |   .5286634   .0922629     5.73   0.000     .3478313    .7094955
            div2 |  -.1215993   .0883278    -1.38   0.169    -.2947185      .05152
            div3 |   .1646094   .0986984     1.67   0.095    -.0288358    .3580547
            div4 |   .1260928   .1198589     1.05   0.293    -.1088262    .3610119
            div5 |    .061736   .1169414     0.53   0.598    -.1674649    .2909369
            div6 |   .0024044   .1283123     0.02   0.985    -.2490832     .253892
            div7 |   .0363753   .1295263     0.28   0.779    -.2174917    .2902422
            div8 |  -.0646132   .1652396    -0.39   0.696    -.3884767    .2592504
            div9 |  -.1421846   .1423669    -1.00   0.318    -.4212186    .1368495
elevat_range_msa |  -.0350281   .0310709    -1.13   0.260    -.0959259    .0258697
  ruggedness_msa |   5.847751   1.800376     3.25   0.001     2.319079    9.376423
      heating_dd |  -.0114928   .0024533    -4.68   0.000    -.0163012   -.0066844
      cooling_dd |  -.0171256   .0048317    -3.54   0.000    -.0265956   -.0076556
          sprawl |   .0050671   .0019119     2.65   0.008     .0013199    .0088144
        _Iyear_2 |    .394952   .0306331    12.89   0.000     .3349123    .4549918
        _Iyear_3 |    .622516   .0314283    19.81   0.000     .5609176    .6841143
           _cons |   3.729259   .6718954     5.55   0.000     2.412368    5.046149
----------------------------------------------------------------------------------
Endogenous: l_ln
Exogenous:  l_pop div2 div3 div4 div5 div6 div7 div8 div9 elevat_range_msa
            ruggedness_msa heating_dd cooling_dd sprawl _Iyear_2 _Iyear_3
            l_pix1835

.                 
.                 xi: genqreg l_vmt l_ln,  q(10) proneness(l_pop div2 div3 div4 div5 
> div6 div7 div8 div9 elevat_range_msa ruggedness_msa heating_dd cooling_dd sprawl i.
> year) instruments( l_pix1835 ) optimize(grid) grid1(0.1(0.01)2)   
i.year            _Iyear_1-3          (_Iyear_1 for year==_1983 omitted)
Grid-search optimization
Number of grid points: 191
 
Generalized Quantile Regression (GQR)
     Observations:                684
 
------------------------------------------------------------------------------
       l_vmt | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
        l_ln |         .9   .5704955     1.58   0.115    -.2181507    2.018151
------------------------------------------------------------------------------
Excluded intstruments: l_pix1835
Proneness variables: l_pop div2 div3 div4 div5 div6 div7 div8 div9 elevat_range_msa r
> uggedness_msa heating_dd cooling_dd sprawl _Iyear_2 _Iyear_3

.                 xi: genqreg l_vmt l_ln,  q(25) proneness(l_pop div2 div3 div4 div5 
> div6 div7 div8 div9 elevat_range_msa ruggedness_msa heating_dd cooling_dd sprawl i.
> year) instruments( l_pix1835 )   optimize(grid) grid1(0.1(0.01)2)                  
>     
i.year            _Iyear_1-3          (_Iyear_1 for year==_1983 omitted)
Grid-search optimization
Number of grid points: 191
 
Generalized Quantile Regression (GQR)
     Observations:                684
 
------------------------------------------------------------------------------
       l_vmt | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
        l_ln |       1.19    .295309     4.03   0.000      .611205    1.768795
------------------------------------------------------------------------------
Excluded intstruments: l_pix1835
Proneness variables: l_pop div2 div3 div4 div5 div6 div7 div8 div9 elevat_range_msa r
> uggedness_msa heating_dd cooling_dd sprawl _Iyear_2 _Iyear_3

.                 xi: genqreg l_vmt l_ln,  q(50) proneness(l_pop div2 div3 div4 div5 
> div6 div7 div8 div9 elevat_range_msa ruggedness_msa heating_dd cooling_dd sprawl i.
> year) instruments( l_pix1835 ) optimize(grid) grid1(0.1(0.01)2)                    
>   
i.year            _Iyear_1-3          (_Iyear_1 for year==_1983 omitted)
Grid-search optimization
Number of grid points: 191
 
Generalized Quantile Regression (GQR)
     Observations:                684
 
------------------------------------------------------------------------------
       l_vmt | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
        l_ln |       .795    .241831     3.29   0.001       .32102     1.26898
------------------------------------------------------------------------------
Excluded intstruments: l_pix1835
Proneness variables: l_pop div2 div3 div4 div5 div6 div7 div8 div9 elevat_range_msa r
> uggedness_msa heating_dd cooling_dd sprawl _Iyear_2 _Iyear_3
    Note: Alternative solutions exist. See e(solutions).

.                 xi: genqreg l_vmt l_ln,  q(75) proneness(l_pop div2 div3 div4 div5 
> div6 div7 div8 div9 elevat_range_msa ruggedness_msa heating_dd cooling_dd sprawl i.
> year) instruments( l_pix1835 )   optimize(grid) grid1(0.1(0.01)2)                 
i.year            _Iyear_1-3          (_Iyear_1 for year==_1983 omitted)
Grid-search optimization
Number of grid points: 191
 
Generalized Quantile Regression (GQR)
     Observations:                684
 
------------------------------------------------------------------------------
       l_vmt | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
        l_ln |        .85   .7974427     1.07   0.286    -.7129591    2.412959
------------------------------------------------------------------------------
Excluded intstruments: l_pix1835
Proneness variables: l_pop div2 div3 div4 div5 div6 div7 div8 div9 elevat_range_msa r
> uggedness_msa heating_dd cooling_dd sprawl _Iyear_2 _Iyear_3

.                 xi: genqreg l_vmt l_ln,  q(90) proneness(l_pop div2 div3 div4 div5 
> div6 div7 div8 div9 elevat_range_msa ruggedness_msa heating_dd cooling_dd sprawl i.
> year) instruments( l_pix1835 )  optimize(grid) grid1(0.1(0.01)2)                   
>     
i.year            _Iyear_1-3          (_Iyear_1 for year==_1983 omitted)
Grid-search optimization
Number of grid points: 191
 
Generalized Quantile Regression (GQR)
     Observations:                684
 
------------------------------------------------------------------------------
       l_vmt | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
        l_ln |       .215   .7197446     0.30   0.765    -1.195673    1.625673
------------------------------------------------------------------------------
Excluded intstruments: l_pix1835
Proneness variables: l_pop div2 div3 div4 div5 div6 div7 div8 div9 elevat_range_msa r
> uggedness_msa heating_dd cooling_dd sprawl _Iyear_2 _Iyear_3
    Note: Alternative solutions exist. See e(solutions).

. 
. 
. * Model 4 *     
. 
.                 xi: ivregress 2sls l_vmt l_pop S_somecollege l_mean_income S_poor S
> _manuf div2 div3 div4 div5 div6 div7 div8 div9 elevat_range_msa ruggedness_msa heat
> ing_dd cooling_dd sprawl i.year (l_ln =  l_pix1835 ), robust                       
>                
i.year            _Iyear_1-3          (_Iyear_1 for year==_1983 omitted)

Instrumental variables 2SLS regression            Number of obs   =        684
                                                  Wald chi2(21)   =   15229.17
                                                  Prob > chi2     =     0.0000
                                                  R-squared       =     0.9476
                                                  Root MSE        =     .31346

----------------------------------------------------------------------------------
                 |               Robust
           l_vmt | Coefficient  std. err.      z    P>|z|     [95% conf. interval]
-----------------+----------------------------------------------------------------
            l_ln |   .6772969   .1334154     5.08   0.000     .4158076    .9387862
           l_pop |   .6091094   .1044057     5.83   0.000     .4044781    .8137408
   S_somecollege |   1.054366   .2085277     5.06   0.000     .6456594    1.463073
   l_mean_income |  -.9114907   .2249557    -4.05   0.000    -1.352396   -.4705856
          S_poor |  -.5755428   .3428252    -1.68   0.093    -1.247468    .0963821
         S_manuf |  -.2003849    .346474    -0.58   0.563    -.8794614    .4786916
            div2 |  -.0377521   .1056365    -0.36   0.721    -.2447959    .1692917
            div3 |   .2962848   .1288286     2.30   0.021     .0437854    .5487843
            div4 |   .1734115   .1364845     1.27   0.204    -.0940932    .4409163
            div5 |   .0551612   .1271769     0.43   0.664    -.1941009    .3044232
            div6 |   .0395777   .1447786     0.27   0.785    -.2441831    .3233385
            div7 |   .1234567   .1497828     0.82   0.410    -.1701122    .4170256
            div8 |   .0185451   .1754118     0.11   0.916    -.3252557    .3623459
            div9 |  -.0659656   .1499259    -0.44   0.660    -.3598149    .2278837
elevat_range_msa |  -.0587405   .0285293    -2.06   0.039    -.1146568   -.0028241
  ruggedness_msa |   5.386386   1.791033     3.01   0.003     1.876025    8.896747
      heating_dd |  -.0118592   .0023088    -5.14   0.000    -.0163844   -.0073339
      cooling_dd |  -.0190494   .0051207    -3.72   0.000    -.0290857    -.009013
          sprawl |   .0068105   .0022156     3.07   0.002     .0024681    .0111529
        _Iyear_2 |   .2323909   .0506287     4.59   0.000     .1331604    .3316214
        _Iyear_3 |   .2958188   .0918686     3.22   0.001     .1157596    .4758781
           _cons |   11.98043   2.084586     5.75   0.000     7.894712    16.06614
----------------------------------------------------------------------------------
Endogenous: l_ln
Exogenous:  l_pop S_somecollege l_mean_income S_poor S_manuf div2 div3 div4 div5
            div6 div7 div8 div9 elevat_range_msa ruggedness_msa heating_dd
            cooling_dd sprawl _Iyear_2 _Iyear_3 l_pix1835

.                 
.                 xi: genqreg l_vmt l_ln,  q(10) proneness(l_pop S_somecollege l_mean
> _income S_poor S_manuf div2 div3 div4 div5 div6 div7 div8 div9 elevat_range_msa rug
> gedness_msa heating_dd cooling_dd sprawl i.year) instruments( l_pix1835 ) optimize(
> grid) grid1(0.1(0.01)2)                
i.year            _Iyear_1-3          (_Iyear_1 for year==_1983 omitted)
Grid-search optimization
Number of grid points: 191
 
Generalized Quantile Regression (GQR)
     Observations:                684
 
------------------------------------------------------------------------------
       l_vmt | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
        l_ln |         .9   .4595245     1.96   0.050    -.0006515    1.800651
------------------------------------------------------------------------------
Excluded intstruments: l_pix1835
Proneness variables: l_pop S_somecollege l_mean_income S_poor S_manuf div2 div3 div4 
> div5 div6 div7 div8 div9 elevat_range_msa ruggedness_msa heating_dd cooling_dd spra
> wl _Iyear_2 _Iyear_3

.                 xi: genqreg l_vmt l_ln,  q(25) proneness(l_pop S_somecollege l_mean
> _income S_poor S_manuf div2 div3 div4 div5 div6 div7 div8 div9 elevat_range_msa rug
> gedness_msa heating_dd cooling_dd sprawl i.year) instruments( l_pix1835 ) optimize(
> grid) grid1(0.1(0.01)2)         
i.year            _Iyear_1-3          (_Iyear_1 for year==_1983 omitted)
Grid-search optimization
Number of grid points: 191
 
Generalized Quantile Regression (GQR)
     Observations:                684
 
------------------------------------------------------------------------------
       l_vmt | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
        l_ln |        .79   .2357588     3.35   0.001     .3279213    1.252079
------------------------------------------------------------------------------
Excluded intstruments: l_pix1835
Proneness variables: l_pop S_somecollege l_mean_income S_poor S_manuf div2 div3 div4 
> div5 div6 div7 div8 div9 elevat_range_msa ruggedness_msa heating_dd cooling_dd spra
> wl _Iyear_2 _Iyear_3

.                 xi: genqreg l_vmt l_ln,  q(50) proneness(l_pop S_somecollege l_mean
> _income S_poor S_manuf div2 div3 div4 div5 div6 div7 div8 div9 elevat_range_msa rug
> gedness_msa heating_dd cooling_dd sprawl i.year) instruments( l_pix1835 ) optimize(
> grid) grid1(0.1(0.01)2)       
i.year            _Iyear_1-3          (_Iyear_1 for year==_1983 omitted)
Grid-search optimization
Number of grid points: 191
 
Generalized Quantile Regression (GQR)
     Observations:                684
 
------------------------------------------------------------------------------
       l_vmt | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
        l_ln |       .735   .5708256     1.29   0.198    -.3837976    1.853798
------------------------------------------------------------------------------
Excluded intstruments: l_pix1835
Proneness variables: l_pop S_somecollege l_mean_income S_poor S_manuf div2 div3 div4 
> div5 div6 div7 div8 div9 elevat_range_msa ruggedness_msa heating_dd cooling_dd spra
> wl _Iyear_2 _Iyear_3
    Note: Alternative solutions exist. See e(solutions).

.                 xi: genqreg l_vmt l_ln,  q(75) proneness(l_pop S_somecollege l_mean
> _income S_poor S_manuf div2 div3 div4 div5 div6 div7 div8 div9 elevat_range_msa rug
> gedness_msa heating_dd cooling_dd sprawl i.year) instruments( l_pix1835 ) optimize(
> grid) grid1(0.1(0.01)2)       
i.year            _Iyear_1-3          (_Iyear_1 for year==_1983 omitted)
Grid-search optimization
Number of grid points: 191
 
Generalized Quantile Regression (GQR)
     Observations:                684
 
------------------------------------------------------------------------------
       l_vmt | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
        l_ln |        .85   .7571691     1.12   0.262    -.6340242    2.334024
------------------------------------------------------------------------------
Excluded intstruments: l_pix1835
Proneness variables: l_pop S_somecollege l_mean_income S_poor S_manuf div2 div3 div4 
> div5 div6 div7 div8 div9 elevat_range_msa ruggedness_msa heating_dd cooling_dd spra
> wl _Iyear_2 _Iyear_3

.                 xi: genqreg l_vmt l_ln,  q(90) proneness(l_pop S_somecollege l_mean
> _income S_poor S_manuf div2 div3 div4 div5 div6 div7 div8 div9 elevat_range_msa rug
> gedness_msa heating_dd cooling_dd sprawl i.year) instruments( l_pix1835 ) optimize(
> grid) grid1(0.1(0.01)2)                        
i.year            _Iyear_1-3          (_Iyear_1 for year==_1983 omitted)
Grid-search optimization
Number of grid points: 191
 
Generalized Quantile Regression (GQR)
     Observations:                684
 
------------------------------------------------------------------------------
       l_vmt | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
        l_ln |       .215   .6900863     0.31   0.755    -1.137544    1.567544
------------------------------------------------------------------------------
Excluded intstruments: l_pix1835
Proneness variables: l_pop S_somecollege l_mean_income S_poor S_manuf div2 div3 div4 
> div5 div6 div7 div8 div9 elevat_range_msa ruggedness_msa heating_dd cooling_dd spra
> wl _Iyear_2 _Iyear_3
    Note: Alternative solutions exist. See e(solutions).

. 
. 
. 
. * Model 5 *     
. 
.                 xi: ivregress 2sls l_vmt l_pop l_pop80 l_pop70 l_pop60 l_pop50 l_po
> p40 l_pop30 l_pop20 S_somecollege l_mean_income S_poor S_manuf div2 div3 div4 div5 
> div6 div7 div8 div9 elevat_range_msa ruggedness_msa heating_dd cooling_dd sprawl i.
> year (l_ln =  l_pix1835 ), robust                                      
i.year            _Iyear_1-3          (_Iyear_1 for year==_1983 omitted)

Instrumental variables 2SLS regression            Number of obs   =        684
                                                  Wald chi2(28)   =   16337.06
                                                  Prob > chi2     =     0.0000
                                                  R-squared       =     0.9523
                                                  Root MSE        =     .29916

----------------------------------------------------------------------------------
                 |               Robust
           l_vmt | Coefficient  std. err.      z    P>|z|     [95% conf. interval]
-----------------+----------------------------------------------------------------
            l_ln |   .7180332   .1398399     5.13   0.000      .443952    .9921144
           l_pop |   .3870944   .1310168     2.95   0.003     .1303061    .6438826
         l_pop80 |    .767765   .2677331     2.87   0.004     .2430177    1.292512
         l_pop70 |  -.5448421   .3205395    -1.70   0.089    -1.173088    .0834038
         l_pop60 |  -.0775066   .2763463    -0.28   0.779    -.6191353    .4641221
         l_pop50 |  -.2778799   .1863996    -1.49   0.136    -.6432164    .0874566
         l_pop40 |     .19416   .1916151     1.01   0.311    -.1813988    .5697187
         l_pop30 |   .0352811   .1596501     0.22   0.825    -.2776274    .3481895
         l_pop20 |   .0894539   .0853319     1.05   0.294    -.0777936    .2567014
   S_somecollege |   1.001442   .2787754     3.59   0.000     .4550518    1.547831
   l_mean_income |  -.6691001   .2138736    -3.13   0.002    -1.088285   -.2499155
          S_poor |  -.4620283   .3418003    -1.35   0.176    -1.131945     .207888
         S_manuf |  -.1503591    .314886    -0.48   0.633    -.7675244    .4668061
            div2 |   -.051918   .0987249    -0.53   0.599    -.2454152    .1415792
            div3 |   .2637617   .1242966     2.12   0.034     .0201449    .5073785
            div4 |   .1286454   .1240409     1.04   0.300    -.1144703    .3717611
            div5 |   .0137593   .1198512     0.11   0.909    -.2211447    .2486633
            div6 |  -.0122763   .1336843    -0.09   0.927    -.2742927    .2497401
            div7 |    .071578   .1322877     0.54   0.588    -.1877011    .3308572
            div8 |  -.0690025   .1802944    -0.38   0.702    -.4223731    .2843681
            div9 |  -.0876065   .1508688    -0.58   0.561    -.3833038    .2080908
elevat_range_msa |  -.0418052   .0295419    -1.42   0.157    -.0997063    .0160959
  ruggedness_msa |   3.775184   1.767174     2.14   0.033     .3115859    7.238782
      heating_dd |  -.0141978   .0023157    -6.13   0.000    -.0187366   -.0096591
      cooling_dd |  -.0231994   .0058737    -3.95   0.000    -.0347116   -.0116871
          sprawl |   .0046465   .0021892     2.12   0.034     .0003557    .0089373
        _Iyear_2 |   .2683958   .0572919     4.68   0.000     .1561058    .3806859
        _Iyear_3 |   .3735748   .1090142     3.43   0.001      .159911    .5872387
           _cons |   10.06898   2.043109     4.93   0.000      6.06456     14.0734
----------------------------------------------------------------------------------
Endogenous: l_ln
Exogenous:  l_pop l_pop80 l_pop70 l_pop60 l_pop50 l_pop40 l_pop30 l_pop20
            S_somecollege l_mean_income S_poor S_manuf div2 div3 div4 div5 div6
            div7 div8 div9 elevat_range_msa ruggedness_msa heating_dd cooling_dd
            sprawl _Iyear_2 _Iyear_3 l_pix1835

.                 
.                 xi: genqreg l_vmt l_ln,  q(10) proneness(l_pop l_pop80 l_pop70 l_po
> p60 l_pop50 l_pop40 l_pop30 l_pop20 S_somecollege l_mean_income S_poor S_manuf div2
>  div3 div4 div5 div6 div7 div8 div9 elevat_range_msa ruggedness_msa heating_dd cool
> ing_dd sprawl i.year) instruments( l_pix1835 )  optimize(grid) grid1(0.1(0.01)2)   
>             
i.year            _Iyear_1-3          (_Iyear_1 for year==_1983 omitted)
Grid-search optimization
Number of grid points: 191
 
Generalized Quantile Regression (GQR)
     Observations:                684
 
------------------------------------------------------------------------------
       l_vmt | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
        l_ln |        .93   .1907989     4.87   0.000      .556041    1.303959
------------------------------------------------------------------------------
Excluded intstruments: l_pix1835
Proneness variables: l_pop l_pop80 l_pop70 l_pop60 l_pop50 l_pop40 l_pop30 l_pop20 S_
> somecollege l_mean_income S_poor S_manuf div2 div3 div4 div5 div6 div7 div8 div9 el
> evat_range_msa ruggedness_msa heating_dd cooling_dd sprawl _Iyear_2 _Iyear_3
    Note: Alternative solutions exist. See e(solutions).

.                 xi: genqreg l_vmt l_ln,  q(25) proneness(l_pop l_pop80 l_pop70 l_po
> p60 l_pop50 l_pop40 l_pop30 l_pop20 S_somecollege l_mean_income S_poor S_manuf div2
>  div3 div4 div5 div6 div7 div8 div9 elevat_range_msa ruggedness_msa heating_dd cool
> ing_dd sprawl i.year) instruments( l_pix1835 ) optimize(grid) grid1(0.1(0.01)2)    
>  
i.year            _Iyear_1-3          (_Iyear_1 for year==_1983 omitted)
Grid-search optimization
Number of grid points: 191
 
Generalized Quantile Regression (GQR)
     Observations:                684
 
------------------------------------------------------------------------------
       l_vmt | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
        l_ln |       .595   .5206834     1.14   0.253    -.4255208    1.615521
------------------------------------------------------------------------------
Excluded intstruments: l_pix1835
Proneness variables: l_pop l_pop80 l_pop70 l_pop60 l_pop50 l_pop40 l_pop30 l_pop20 S_
> somecollege l_mean_income S_poor S_manuf div2 div3 div4 div5 div6 div7 div8 div9 el
> evat_range_msa ruggedness_msa heating_dd cooling_dd sprawl _Iyear_2 _Iyear_3
    Note: Alternative solutions exist. See e(solutions).

.                 xi: genqreg l_vmt l_ln,  q(50) proneness(l_pop l_pop80 l_pop70 l_po
> p60 l_pop50 l_pop40 l_pop30 l_pop20 S_somecollege l_mean_income S_poor S_manuf div2
>  div3 div4 div5 div6 div7 div8 div9 elevat_range_msa ruggedness_msa heating_dd cool
> ing_dd sprawl i.year) instruments( l_pix1835 )  optimize(grid) grid1(0.1(0.01)2)  
i.year            _Iyear_1-3          (_Iyear_1 for year==_1983 omitted)
Grid-search optimization
Number of grid points: 191
 
Generalized Quantile Regression (GQR)
     Observations:                684
 
------------------------------------------------------------------------------
       l_vmt | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
        l_ln |       .765   .5179968     1.48   0.140     -.250255    1.780255
------------------------------------------------------------------------------
Excluded intstruments: l_pix1835
Proneness variables: l_pop l_pop80 l_pop70 l_pop60 l_pop50 l_pop40 l_pop30 l_pop20 S_
> somecollege l_mean_income S_poor S_manuf div2 div3 div4 div5 div6 div7 div8 div9 el
> evat_range_msa ruggedness_msa heating_dd cooling_dd sprawl _Iyear_2 _Iyear_3
    Note: Alternative solutions exist. See e(solutions).

.                 xi: genqreg l_vmt l_ln,  q(75) proneness(l_pop l_pop80 l_pop70 l_po
> p60 l_pop50 l_pop40 l_pop30 l_pop20 S_somecollege l_mean_income S_poor S_manuf div2
>  div3 div4 div5 div6 div7 div8 div9 elevat_range_msa ruggedness_msa heating_dd cool
> ing_dd sprawl i.year) instruments( l_pix1835 )   optimize(grid) grid1(0.1(0.01)2) 
i.year            _Iyear_1-3          (_Iyear_1 for year==_1983 omitted)
Grid-search optimization
Number of grid points: 191
 
Generalized Quantile Regression (GQR)
     Observations:                684
 
------------------------------------------------------------------------------
       l_vmt | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
        l_ln |        .85   .7053615     1.21   0.228    -.5324831    2.232483
------------------------------------------------------------------------------
Excluded intstruments: l_pix1835
Proneness variables: l_pop l_pop80 l_pop70 l_pop60 l_pop50 l_pop40 l_pop30 l_pop20 S_
> somecollege l_mean_income S_poor S_manuf div2 div3 div4 div5 div6 div7 div8 div9 el
> evat_range_msa ruggedness_msa heating_dd cooling_dd sprawl _Iyear_2 _Iyear_3

.                 xi: genqreg l_vmt l_ln,  q(90) proneness(l_pop l_pop80 l_pop70 l_po
> p60 l_pop50 l_pop40 l_pop30 l_pop20 S_somecollege l_mean_income S_poor S_manuf div2
>  div3 div4 div5 div6 div7 div8 div9 elevat_range_msa ruggedness_msa heating_dd cool
> ing_dd sprawl i.year) instruments( l_pix1835 ) optimize(grid) grid1(0.1(0.01)2)    
>     
i.year            _Iyear_1-3          (_Iyear_1 for year==_1983 omitted)
Grid-search optimization
Number of grid points: 191
 
Generalized Quantile Regression (GQR)
     Observations:                684
 
------------------------------------------------------------------------------
       l_vmt | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
        l_ln |       .215   .6664627     0.32   0.747    -1.091243    1.521243
------------------------------------------------------------------------------
Excluded intstruments: l_pix1835
Proneness variables: l_pop l_pop80 l_pop70 l_pop60 l_pop50 l_pop40 l_pop30 l_pop20 S_
> somecollege l_mean_income S_poor S_manuf div2 div3 div4 div5 div6 div7 div8 div9 el
> evat_range_msa ruggedness_msa heating_dd cooling_dd sprawl _Iyear_2 _Iyear_3
    Note: Alternative solutions exist. See e(solutions).

.         
. 
.                 
.         
. log off 
      name:  <unnamed>
       log:  /Users/abhram/Library/CloudStorage/Dropbox/Research/trA/replication/tabl
> e_4.log
  log type:  text
 paused on:  10 May 2023, 17:52:00
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